Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.

Efforts to enhance the accuracy of protein sequence classification are of utmost importance in driving forward biological analyses and facilitating significant medical advancements. This study presents a cutting-edge model called ProtICNN-BiLSTM, which combines attention-based Improved Convolutional Neural Networks (ICNN) and Bidirectional Long Short-Term Memory (BiLSTM) units seamlessly. Our main goal is to improve the accuracy of protein sequence classification by carefully optimizing performance through Bayesian Optimisation. ProtICNN-BiLSTM combines the power of CNN and BiLSTM architectures to effectively capture local and global protein sequence dependencies. In the proposed model, the ICNN component uses convolutional operations to identify local patterns. Captures long-range associations by analyzing sequence data forward and backwards. In advanced biological studies, Bayesian Optimisation optimizes model hyperparameters for efficiency and robustness. The model was extensively confirmed with PDB-14,189 and other protein data. We found that ProtICNN-BiLSTM outperforms traditional categorization models. Bayesian Optimization's fine-tuning and seamless integration of local and global sequence information make it effective. The precision of ProtICNN-BiLSTM improves comparative protein sequence categorization. The study improves computational bioinformatics for complex biological analysis. Good results from the ProtICNN-BiLSTM model improve protein sequence categorization. This powerful tool could improve medical and biological research. The breakthrough protein sequence classification model is ProtICNN-BiLSTM. Bayesian optimization, ICNN, and BiLSTM analyze biological data accurately.

Joint Placement and Device Association of UAV Base Stations in IoT Networks.

Drone base stations (DBSs) have received significant research interest in recent years. They provide a flexible and cost-effective solution to improve the coverage, connectivity, quality of service (QoS), and energy efficiency of large-area Internet of Things (IoT) networks. However, as DBSs are costly and power-limited devices, they require an efficient scheme for their deployment in practical networks. This work proposes a realistic mathematical model for the joint optimization problem of DBS placement and IoT users' assignment in a massive IoT network scenario. The optimization goal is to maximize the connectivity of IoT users by utilizing the minimum number of DBS, while satisfying practical network constraints. Such an optimization problem is NP-hard, and the optimal solution has a complexity exponential to the number of DBSs and IoT users in the network. Furthermore, this work also proposes a linearization scheme and a low-complexity heuristic to solve the problem in polynomial time. The simulations are performed for a number of network scenarios, and demonstrate that the proposed heuristic is numerically accurate and performs close to the optimal solution.

CoSiM-RPO: Cooperative Routing with Sink Mobility for Reliable and Persistent Operation in Underwater Acoustic Wireless Sensor Networks.

An efficient algorithm for the persistence operation of data routing is crucial due to the uniqueness and challenges of the aqueous medium of the underwater acoustic wireless sensor networks (UA-WSNs). The existing multi-hop algorithms have a high energy cost, data loss, and less stability due to many forwarders for a single-packet delivery. In order to tackle these constraints and limitations, two algorithms using sink mobility and cooperative technique for UA-WSNs are devised. The first one is sink mobility for reliable and persistence operation (SiM-RPO) in UA-WSNs, and the second is the enhanced version of the SiM-RPO named CoSiM-RPO, which utilizes the cooperative technique for better exchanging of the information and minimizes data loss probability. To cover all of the network through mobile sinks (MSs), the division of the network into small portions is accomplished. The path pattern is determined for MSs in a manner to receive data even from a single node in the network. The MSs pick the data directly from the nodes and check them for the errors. When erroneous data are received at the MS, then the relay cooperates to receive correct data. The proposed algorithm boosts the network lifespan, throughput, delay, and stability more than the existing counterpart schemes.

Towards Void Hole Alleviation by Exploiting the Energy Efficient Path and by Providing the Interference-Free Proactive Routing Protocols in IoT Enabled Underwater WSNs.

Nowadays, the Internet of Things enabled Underwater Wireless Sensor Network (IoT-UWSN) is suffering from serious performance restrictions, i.e., high End to End (E2E) delay, low energy efficiency, low data reliability, etc. The necessity of efficient, reliable, collision and interference-free communication has become a challenging task for the researchers. However, the minimum Energy Consumption (EC) and low E2E delay increase the performance of the IoT-UWSN. Therefore, in the current work, two proactive routing protocols are presented, namely: Bellman⁻Ford Shortest Path-based Routing (BF-SPR-Three) and Energy-efficient Path-based Void hole and Interference-free Routing (EP-VIR-Three). Then we formalized the aforementioned problems to accomplish the reliable data transmission in Underwater Wireless Sensor Network (UWSN). The main objectives of this paper include minimum EC, interference-free transmission, void hole avoidance and high Packet Delivery Ratio (PDR). Furthermore, the algorithms for the proposed routing protocols are presented. Feasible regions using linear programming are also computed for optimal EC and to enhance the network lifespan. Comparative analysis is also performed with state-of-the-art proactive routing protocols. In the end, extensive simulations have been performed to authenticate the performance of the proposed routing protocols. Results and discussion disclose that the proposed routing protocols outperformed the counterparts significantly.

Electricity Load and Price Forecasting Using Jaya-Long Short Term Memory (JLSTM) in Smart Grids.

In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.

An implementation of normal distribution based segmentation and entropy controlled features selection for skin lesion detection and classification.

BACKGROUND: Melanoma is the deadliest type of skin cancer with highest mortality rate. However, the annihilation in its early stage implies a high survival rate therefore, it demands early diagnosis. The accustomed diagnosis methods are costly and cumbersome due to the involvement of experienced experts as well as the requirements for the highly equipped environment. The recent advancements in computerized solutions for this diagnosis are highly promising with improved accuracy and efficiency. METHODS: In this article, a method for the identification and classification of the lesion based on probabilistic distribution and best features selection is proposed. The probabilistic distribution such as normal distribution and uniform distribution are implemented for segmentation of lesion in the dermoscopic images. Then multi-level features are extracted and parallel strategy is performed for fusion. A novel entropy-based method with the combination of Bhattacharyya distance and variance are calculated for the selection of best features. Only selected features are classified using multi-class support vector machine, which is selected as a base classifier. RESULTS: The proposed method is validated on three publicly available datasets such as PH2, ISIC (i.e. ISIC MSK-2 and ISIC UDA), and Combined (ISBI 2016 and ISBI 2017), including multi-resolution RGB images and achieved accuracy of 97.5%, 97.75%, and 93.2%, respectively. CONCLUSION: The base classifier performs significantly better on proposed features fusion and selection method as compared to other methods in terms of sensitivity, specificity, and accuracy. Furthermore, the presented method achieved satisfactory segmentation results on selected datasets.

Retransmission Avoidance for Reliable Data Delivery in Underwater WSNs.

The energy-efficient and reliable delivery of data packets in resource constraint underwater wireless sensor networks (UWSNs) is one of the key considerations to enhance the network lifetime. The traditional re-transmissions approach consumes the node battery and increases the communication overhead, which results in congestion and affects the reliable data packet delivery in the network. To ensure the reliability and conserve the node battery, in this paper, we propose adaptive forwarding layer multipath power control routing protocol to reduce the energy dissipation, achieve the data reliability and avoid the energy hole problem. In order to achieve the reliability, tree based topology is exploited to direct multiple copies of the data packet towards the surface through cross nodes in the network. The energy dissipation is reduced by a substantial amount with the selection of low noise path between the source and the destination including the information of neighbors of the potential forwarder node. Extensive simulation results show that our proposed work outperforms the compared existing scheme in terms of energy efficiency and packet received ratio (PRR).

LMBiS-Net: A lightweight bidirectional skip connection based multipath CNN for retinal blood vessel segmentation.

Blinding eye diseases are often related to changes in retinal structure, which can be detected by analysing retinal blood vessels in fundus images. However, existing techniques struggle to accurately segment these delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on specific operations can limit its ability to capture crucial details such as the edges of the vessel. This paper introduces LMBiS-Net, a lightweight convolutional neural network designed for the segmentation of retinal vessels. LMBiS-Net achieves exceptional performance with a remarkably low number of learnable parameters (only 0.172 million). The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. In addition, we have optimised the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimisation significantly reduces training time and improves computational efficiency. To assess LMBiS-Net's robustness and ability to generalise to unseen data, we conducted comprehensive evaluations on four publicly available datasets: DRIVE, STARE, CHASE_DB1, and HRF The proposed LMBiS-Net achieves significant performance metrics in various datasets. It obtains sensitivity values of 83.60%, 84.37%, 86.05%, and 83.48%, specificity values of 98.83%, 98.77%, 98.96%, and 98.77%, accuracy (acc) scores of 97.08%, 97.69%, 97.75%, and 96.90%, and AUC values of 98.80%, 98.82%, 98.71%, and 88.77% on the DRIVE, STARE, CHEASE_DB, and HRF datasets, respectively. In addition, it records F1 scores of 83.43%, 84.44%, 83.54%, and 78.73% on the same datasets. Our evaluations demonstrate that LMBiS-Net achieves high segmentation accuracy (acc) while exhibiting both robustness and generalisability across various retinal image datasets. This combination of qualities makes LMBiS-Net a promising tool for various clinical applications.

Advanced machine learning approach for DoS attack resilience in internet of vehicles security.

Recent years have witnessed security as a great concern in vehicular networks (VANET). Particularly, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks can jeopardize the network by broadcasting a storm of packets. Correspondingly, the network resources are jammed with malicious traffic. In this connection, the existing research presented various techniques to cope with DoS and DDoS attacks. Different from those traditional approaches, this study proposes an Intelligent Intrusion Detection System (IDS) by leveraging Machine Learning (ML). The proposed IDS utilizes a publicly available dataset on the application layer for mitigating DDoS attacks. The designed ML-based IDS relies on combining both the Random Projection (RP) and Randomized Matrix Factorization (RMF) methods to achieve the best results for enhancing the detection capabilities of the IDS. This amalgamation enhances the system's detection capabilities by extracting and analyzing meaningful features from network traffic data. Experimental validation of our approach involves a comprehensive evaluation of various ML models, including Extra Tree Classifier (ETC), Logistic Regression (LR), and Random Forest (RF). Remarkably, the combined accuracy of these models yields an average system accuracy of 0.98, surpassing existing methods. Unlike conventional approaches, our proposed IDS excels in efficiency and exhibits notable performance in detecting DoS and DDoS attacks in VANET. This proficiency ensures the integrity and safety of vehicle communications. Thus, our research substantially contributes to the vehicular network security field. The presented findings establish a foundation for future advancements in securing connected vehicles.

Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model.

Introduction: Cardiovascular disease (CVD) stands as a pervasive catalyst for illness and mortality on a global scale, underscoring the imperative for sophisticated prediction methodologies within the ambit of healthcare data analysis. The vast volume of medical data available necessitates effective data mining techniques to extract valuable insights for decision-making and prediction. While machine learning algorithms are commonly employed for CVD diagnosis and prediction, the high dimensionality of datasets poses a performance challenge. Methods: This research paper presents a novel hybrid model for predicting CVD, focusing on an optimal feature set. The proposed model encompasses four main stages namely: preprocessing, feature extraction, feature selection (FS), and classification. Initially, data preprocessing eliminates missing and duplicate values. Subsequently, feature extraction is performed to address dimensionality issues, utilizing measures such as central tendency, qualitative variation, degree of dispersion, and symmetrical uncertainty. FS is optimized using the self-improved Aquila optimization approach. Finally, a hybridized model combining long short-term memory and a quantum neural network is trained using the selected features. An algorithm is devised to optimize the LSTM model's weights. Performance evaluation of the proposed approach is conducted against existing models using specific performance measures. Results: Far dataset-1, accuracy-96.69%, sensitivity-96.62%, specifity-96.77%, precision-96.03%, recall-97.86%, F1-score-96.84%, MCC-96.37%, NPV-96.25%, FPR-3.2%, FNR-3.37% and for dataset-2, accuracy-95.54%, sensitivity-95.86%, specifity-94.51%, precision-96.03%, F1-score-96.94%, MCC-93.03%, NPV-94.66%, FPR-5.4%, FNR-4.1%. The findings of this study contribute to improved CVD prediction by utilizing an efficient hybrid model with an optimized feature set. Discussion: We have proven that our method accurately predicts cardiovascular disease (CVD) with unmatched precision by conducting extensive experiments and validating our methodology on a large dataset of patient demographics and clinical factors. QNN and LSTM frameworks with Aquila feature tuning increase forecast accuracy and reveal cardiovascular risk-related physiological pathways. Our research shows how advanced computational tools may alter sickness prediction and management, contributing to the emerging field of machine learning in healthcare. Our research used a revolutionary methodology and produced significant advances in cardiovascular disease prediction.

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.

The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.

Range aware message drop policy for delay tolerant networks.

In delay tolerant networks (DTNs) the messages are often not delivered to the destination due to a lack of end-to-end connectivity. In such cases, the messages are stored in the buffer for a long time and are transmitted when the nodes come into the range of each other. The buffer size of each node has a limited capacity, and it cannot accommodate the new incoming message when the buffer memory is full, and as a result network congestion occurs. This leads to a low delivery probability and thus increases the overhead ratio. In this research work, a new buffer management scheme called Range Aware Drop (RAD) is proposed which considers metrics such as message size and time to live (TTL). RAD utilizes TTL as an important metric and as a result, reduces the unnecessary message drop. Simulation results reveal that RAD performs significantly better than drop oldest (DOA) and size aware drop (SAD) in terms of delivery probability and overhead ratio. The obtained results also revealed that the hop-count average of SAD is 3.9 and DOA is 3.4 while the hop-count average of RAD is just 1.7. Also, the message drop ratio of the RAD is 36.2% while SAD and DOA have message drop ratios of 73.3% and 84.9% respectively.

Pashto script and graphics detection in camera captured Pashto document images using deep learning model.

Layout analysis is the main component of a typical Document Image Analysis (DIA) system and plays an important role in pre-processing. However, regarding the Pashto language, the document images have not been explored so far. This research, for the first time, examines Pashto text along with graphics and proposes a deep learning-based classifier that can detect Pashto text and graphics per document. Another notable contribution of this research is the creation of a real dataset, which contains more than 1,000 images of the Pashto documents captured by a camera. For this dataset, we applied the convolution neural network (CNN) following a deep learning technique. Our intended method is based on the development of the advanced and classical variant of Faster R-CNN called Single-Shot Detector (SSD). The evaluation was performed by examining the 300 images from the test set. Through this way, we achieved a mean average precision (mAP) of 84.90%.

Markov chain-based impact analysis of the pandemic Covid-19 outbreak on global primary energy consumption mix.

The historic evolution of global primary energy consumption (GPEC) mix, comprising of fossil (liquid petroleum, gaseous and coal fuels) and non-fossil (nuclear, hydro and other renewables) energy sources while highlighting the impact of the novel corona virus 2019 pandemic outbreak, has been examined through this study. GPEC data of 2005-2021 has been taken from the annually published reports by British Petroleum. The equilibrium state, a property of the classical predictive modeling based on Markov chain, is employed as an investigative tool. The pandemic outbreak has proved to be a blessing in disguise for global energy sector through, at least temporarily, reducing the burden on environment in terms of reducing demand for fossil energy sources. Some significant long term impacts of the pandemic occurred in second and third years (2021 and 2022) after its outbreak in 2019 rather than in first year (2020) like the penetration of other energy sources along with hydro and renewable ones in GPEC. Novelty of this research lies within the application of the equilibrium state feature of compositional Markov chain based prediction upon GPEC mix. The analysis into the past trends suggests the advancement towards a better global energy future comprising of cleaner fossil resources (mainly natural gas), along with nuclear, hydro and renewable ones in the long run.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

Background: The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. Objective: This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. Methods: The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. Results: The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. Conclusion: This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

A residual connection enabled deep neural network model for optic disk and optic cup segmentation for glaucoma diagnosis.

Glaucoma diagnosis at an early stage is vital for the timely initiation of its treatment for and preventing possible vision loss. For glaucoma diagnosis, an accurate estimation of the cup-to-disk ratio (CDR) is required. The current automatic CDR computation techniques attribute lower accuracy and higher complexity, which are important considerations for diagnostics system design to be used for such critical diagnoses. The current methods involve a deeper deep learning model, comprising a large number of parameters, which results in higher system complexity and training/testing time. To address these challenges, this paper proposes a Residual Connection (non-identity)-based Deep Neural Network (RC-DNN), which is based on non-identity residual connectivity for joint optic disk (OD) and optic cup (OC) detection. The proposed model is emboldened by efficient residual connectivity, which is beneficial in several ways. First, the model is efficient and can perform simultaneous segmentation of the OC and OD. Second, the efficient residual information flow permeates the vanishing gradient problem which results in faster converges of the model. Third, feature inspiration empowers the network to perform the segmentation with only a few network layers. We performed a comprehensive performance evaluation of the developed model based on its training in RIM-ONE and DRISHTIGS databases. For OC segmentation, for the images (test set) from {DRISHTI-GS and RIM-ONE} datasets, our proposed model achieves the dice coefficient, Jaccard coefficient, sensitivity, specificity, and balanced accuracy of {92.62, 86.52}, {86.87, 77.54}, {94.21, 95.36}, {99.83, 99.639}, and {94.2, 98.9}, respectively. These experimental results indicate that the developed model provides significant performance enhancement for joint OC and OD segmentation. Additionally, the reduced computational complexity based on reduced model parameters and higher segmentation accuracy provides the additional features of efficacy, robustness, and reliability of the developed model. These attributes of the developed model advocate for its deployment of population-scale glaucoma screening programs.

A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images.

The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model's guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model's superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.

Hybrid Diagnostic Model for Improved COVID-19 Detection in Lung Radiographs Using Deep and Traditional Features.

A recently discovered coronavirus (COVID-19) poses a major danger to human life and health across the planet. The most important step in managing and combating COVID-19 is to accurately screen and diagnose affected people. The imaging technology of lung X-ray is a useful imaging identification/detection approach among them. The help of such computer-aided machines and diagnoses to examine lung X-ray images of COVID-19 instances can give supplemental assessment ideas to specialists, easing their workload to some level. The novel concept of this study is a hybridized approach merging pertinent manual features with deep spatial features for the classification of COVID-19. Further, we employed traditional transfer learning techniques in this investigation, utilizing four different pre-trained CNN-based deep learning models, with the Inception model showing a reasonably accurate result and a diagnosis accuracy of 82.17%. We provide a successful diagnostic approach that blends deep characteristics with machine learning classification to further increase clinical performance. It employs a complete diagnostic model. Two datasets were used to test the suggested approach, and it did quite well on several of them. On 1102 lung X-ray scans, the model was originally evaluated. The results of the experiments indicate that the suggested SVM model has a diagnostic accuracy of 95.57%. When compared to the Xception model's baseline, the diagnostic accuracy had risen by 17.58 percent. The sensitivity, specificity, and AUC of the proposed models were 95.37 percent, 95.39%, and 95.77%, respectively. To show the adaptability of our approach, we also verified our proposed model on other datasets. Finally, we arrived at results that were conclusive. When compared to research of a comparable kind, our suggested CNN model has a greater accuracy of classification and diagnostic effectiveness.