Proposal-Free Fully Convolutional Network: Object Detection Based on a Box Map.

Region proposal-based detectors, such as Region-Convolutional Neural Networks (R-CNNs), Fast R-CNNs, Faster R-CNNs, and Region-Based Fully Convolutional Networks (R-FCNs), employ a two-stage process involving region proposal generation followed by classification. This approach is effective but computationally intensive and typically slower than proposal-free methods. Therefore, region proposal-free detectors are becoming popular to balance accuracy and speed. This paper proposes a proposal-free, fully convolutional network (PF-FCN) that outperforms other state-of-the-art, proposal-free methods. Unlike traditional region proposal-free methods, PF-FCN can generate a "box map" based on regression training techniques. This box map comprises a set of vectors, each designed to produce bounding boxes corresponding to the positions of objects in the input image. The channel and spatial contextualized sub-network are further designed to learn a "box map". In comparison to renowned proposal-free detectors such as CornerNet, CenterNet, and You Look Only Once (YOLO), PF-FCN utilizes a fully convolutional, single-pass method. By reducing the need for fully connected layers and filtering center points, the method considerably reduces the number of trained parameters and optimizes the scalability across varying input sizes. Evaluations of benchmark datasets suggest the effectiveness of PF-FCN: the proposed model achieved an mAP of 89.6% on PASCAL VOC 2012 and 71.7% on MS COCO, which are higher than those of the baseline Fully Convolutional One-Stage Detector (FCOS) and other classical proposal-free detectors. The results prove the significance of proposal-free detectors in both practical applications and future research.

Cerebral versus cortical visual impairment: eliminating the conflict and renewing the terminology.

The inconsistency in terminology for Cortical Visual Impairment or Cerebral Visual Impairment presents challenges: (1) different levels of changes in visual pathway and other cerebral areas do not allow discrimination; (2) different visual and oculomotor aspects are not adequately considered. We open a debate to consider a more appropriate diagnosis.

TSAF-Net: a rotated two-stage Cnaphalocrocis medinalis damage detection method based on anchor-free arbitrary-oriented proposal network.

BACKGROUND: Cnaphalocrocis medinalis (C.medinalis) is an agricultural pest with recurrent outbreaks. The investigation into automated pest and disease detection technology holds significant value for in-field surveys. Current generic detection methods are inadequate due to arbitrary orientations and a wide range of aspect ratios in damage symptoms. To tackle these issues, we put forward a rotated two-stage detection method for in-field C.medinalis surveys. This method relies on an anchor-free rotated region proposal network (AF-R2PN), bypassing the need for hyper-parameter optimization induced by predefined anchor boxes. An in-field C.medinalis dataset is constructed during on-site pest surveys to validate the effectiveness of our method. RESULTS: The experimental results show that our method can accomplish 80% average precision (AP), surpassing the corresponding horizontal detector by 2.3%. The visualization results of our work showcase its exceptional localization capability over generic detection methods, facilitating inspection by plant protectors. Meanwhile, our proposed method outperforms other state-of-the-art rotated detection algorithms. The AF-R2PN module can generate superior arbitrary-oriented proposals even with a decreased number of proposals, balancing inference speed and detection performance among other rotated two-stage methods. CONCLUSION: The proposed method exhibits superiority in detecting C. medinalis damage under complex field conditions. It provides greater practical applicability during in-field surveys, enhancing their efficiency and coverage. The findings hold significance for pest and disease monitoring, providing important technical support for agricultural production. © 2024 Society of Chemical Industry.

The Impact of Spironolactone Co-administration on Cyclosporin Initial Dosage Optimization for Pediatric Refractory Nephrotic Syndrome.

OBJECTIVES: Cyclosporin has been used for the treatment of pediatric refractory nephrotic syndrome (PRNS). However, the narrow therapeutic window and large pharmacokinetic variability make it difficult to individualize cyclosporin administration. Meanwhile, spironolactone has been reported to affect cyclosporin metabolism in PRNS patients. This study aims to explore the initial dosage optimization of cyclosporin in PRNS based on the impact of spironolactone co-administration. METHODS: Monte Carlo simulation based on a previously established cyclosporin population pharmacokinetic model for PRNS was used to design cyclosporin dosing regimen. RESULTS: In this study, the probability of drug concentration reaching the target and the convenience of times of administration were considered comprehensively. The optimal administration regimen in PRNS without spironolactone was 6, 5, 4 and 3 mg/kg cyclosporin split into two doses for the body weight of 5-8, 8-18, 18-46 and 46-70 kg, respectively. The optimal administration regimen in PRNS with spironolactone was 4, 3, 2 mg/kg cyclosporin split into two doses for body weight of 5-14, 14-65, and 65-70 kg, respectively. CONCLUSION: The cyclosporin dosing regimen for PRNS based on Monte Carlo simulation was systematically developed and the initial dosage optimization of cyclosporin in PRNS was recommended for the first time.

Automatic nutrient estimator: distributing nutrient solution in hydroponic plants based on plant growth.

Background: The primary objective is to address the specific needs of plants at different growth stages by delivering precise nutrient concentrations tailored to their developmental requirements. Challenges such as uneven nutrient distribution, fluctuations in pH and electrical conductivity, and inadequate nutrient delivery pose potential hindrances to achieving optimal plant health and yield in hydroponic systems. By overcoming these challenges, the hydroponic farming community aims to enhance the accuracy of nutrient dosing, streamline automation processes, and minimize resource wastage. Hydroponics, a cultivation technique without soil, facilitates the growth of organic vegetation while concurrently minimizing water use and eliminating the necessity for pesticides. In order to achieve effective cultivation of hydroponic plants, it is essential to maintain a controlled environment that encompasses essential factors such as temperature, carbon dioxide (CO2) levels, oxygen availability, and appropriate lighting conditions. Additionally, it is crucial to ensure the provision of vital nutrients to maximize output and productivity. Due to the demanding nature of a hydroponic farmer's schedule, it is necessary to minimize the amount of time dedicated to nutrient management, as well as pH and EC adjustments. Methods: In order to determine and deliver the proper amount of vital nutrients, such as nitrogen, phosphorus, and potassium, based on the plant growth stage, we presented an automatic hydroponic nutrient estimator in this system. We noticed that the plant's nutrient consumption varies depending on its stage of growth according to plant psychology. Four peristaltic pumps with the necessary sensors are controlled by an Arduino board in the suggested system. Both filling and draining the water are done using each pump. To identify the plant stage, we apply the Plant Growth Stage Identification algorithm to encompass the seedling, vegetative, flowers, and fruit stages. Results: The experimental results reveal that the Growth Stage Identification algorithm obtains 97.5% accuracy for the first 5 weeks with 1,715 ppm of nutrition ingestion, identifying the vegetative state. The flowering stage is identified with 97.5% accuracy in the 6-9th week with 2,380 ppm of nutrition consumption, and the fruiting location is determined with 99.4% accuracy in the last 10-15th week with 2,730 ppm of nutrition consumption.

Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets.

BACKGROUND: Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images. METHODS: In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules. RESULTS: The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%. CONCLUSION: A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.

A modified 3D RPN for detecting lung nodules on CT images that exhibited greater sensitivity and CPM than did several previously reported CAD detection models was established.

Where does the EU-path on new genomic techniques lead us?

Recently, the European Commission (EC) published a regulatory proposal on plants generated with new genomic techniques (NGTs) (5 July 2023). According to this proposal, NGT plant applications are categorized into category 1 NGT (NGT1) and category 2 NGT (NGT2) based on their molecular characteristics, which diverges from the current legislation centered around Directive 2001/18/EC. To demonstrate where the path of the proposal leads to in practice, we applied the proposed criteria for categorization to a list of NGT plant applications currently in the commercialization pipeline. Combining literature research and a descriptive statistical approach, we can show that 94% of the plant applications affected by the EC proposal, would be classified as NGT1 and thus would receive market approval without risk assessment, monitoring, and sufficient labeling provisions. The remaining 6% of applications would be classified as NGT2 plants, for which, in deviation from the current regulation, an adapted risk assessment is proposed. Screening of the intended traits in the pipeline highlights that certain NGT1 plants can pose similar environmental risks (e.g., invasiveness) to other genetically modified organisms (GMOs), as defined in Directive 2001/18/EC. For example, NGT1 applications based on RNA interference technology can exhibit insecticidal effects with potential side effects on non-target organisms (i.e., other insects). Our quantitative and case-specific elaboration of how the current EC regulatory proposal would affect the environment, health, and consumer protection will be informative for decision-makers and politicians.

Automated peripancreatic vessel segmentation and labeling based on iterative trunk growth and weakly supervised mechanism.

Peripancreatic vessel segmentation and anatomical labeling are pivotal aspects in aiding surgical planning and prognosis for patients with pancreatic tumors. Nevertheless, prevailing techniques often fall short in achieving satisfactory segmentation performance for the peripancreatic vein (PPV), leading to predictions characterized by poor integrity and connectivity. Besides, unsupervised labeling algorithms usually cannot deal with complex anatomical variation while fully supervised methods require a large number of voxel-wise annotations for training, which is very labor-intensive and time-consuming. To address these two problems, we propose an Automated Peripancreatic vEssel Segmentation and lAbeling (APESA) framework, to not only highly improve the segmentation performance for PPV, but also efficiently identify the peripancreatic artery (PPA) branches. There are two core modules in our proposed APESA framework: iterative trunk growth module (ITGM) for vein segmentation and weakly supervised labeling mechanism (WSLM) for artery labeling. The ITGM is composed of a series of iterative submodules, each of which chooses the largest connected component of the previous PPV segmentation as the trunk of a tree structure, seeks for the potential missing branches around the trunk by our designed branch proposal network, and facilitates trunk growth under the connectivity constraint. The WSLM incorporates the rule-based pseudo label generation with less expert participation, an anatomical labeling network to learn the branch distribution voxel by voxel, and adaptive radius-based postprocessing to refine the branch structures of the labeling predictions. Our achieved Dice of 94.01% for PPV segmentation on our collected dataset represents an approximately 10% accuracy improvement compared to state-of-the-art methods. Additionally, we attained a Dice of 97.01% for PPA segmentation and competitive labeling performance for PPA labeling compared to prior works. Our source codes will be publicly available at https://github.com/ZouLiwen-1999/APESA.

MSNSegNet: attention-based multi-shape nuclei instance segmentation in histopathology images.

In clinical research, the segmentation of irregularly shaped nuclei, particularly in mesenchymal areas like fibroblasts, is crucial yet often neglected. These irregular nuclei are significant for assessing tissue repair in immunotherapy, a process involving neovascularization and fibroblast proliferation. Proper segmentation of these nuclei is vital for evaluating immunotherapy's efficacy, as it provides insights into pathological features. However, the challenge lies in the pronounced curvature variations of these non-convex nuclei, making their segmentation more difficult than that of regular nuclei. In this work, we introduce an undefined task to segment nuclei with both regular and irregular morphology, namely multi-shape nuclei segmentation. We propose a proposal-based method to perform multi-shape nuclei segmentation. By leveraging the two-stage structure of the proposal-based method, a powerful refinement module with high computational costs can be selectively deployed only in local regions, improving segmentation accuracy without compromising computational efficiency. We introduce a novel self-attention module to refine features in proposals for the sake of effectiveness and efficiency in the second stage. The self-attention module improves segmentation performance by capturing long-range dependencies to assist in distinguishing the foreground from the background. In this process, similar features get high attention weights while dissimilar ones get low attention weights. In the first stage, we introduce a residual attention module and a semantic-aware module to accurately predict candidate proposals. The two modules capture more interpretable features and introduce additional supervision through semantic-aware loss. In addition, we construct a dataset with a proportion of non-convex nuclei compared with existing nuclei datasets, namely the multi-shape nuclei (MsN) dataset. Our MSNSegNet method demonstrates notable improvements across various metrics compared to the second-highest-scoring methods. For all nuclei, the D i c e score improved by approximately 1.66 % , A J I by about 2.15 % , and D i c e obj by roughly 0.65 % . For non-convex nuclei, which are crucial in clinical applications, our method's A J I improved significantly by approximately 3.86 % and D i c e obj by around 2.54 % . These enhancements underscore the effectiveness of our approach on multi-shape nuclei segmentation, particularly in challenging scenarios involving irregularly shaped nuclei.

An idea to explore: Cultivating the art of proposal writing among graduate students.

Proposal writing is an essential requirement for making progress in academics. Learning this skill necessitates support from a mentor to cultivate effective habits. It entails effective strategies from graduate students, such as literature reading and using online tools. Additionally, they must develop an understanding of resource accountability, system thinking, and considering deadlines as a driving force. Good practices for effective proposal writing also involve planning to summarize the work done in the field. Moreover, it requires ideal mentor support by providing timely assistance, helping students overcome impostor syndrome, sharing successful proposals, and creating a cooperative environment.

The landscape of penile cancer research in Germany and Austria: a survey among professors in academic centers holding chair positions and results of a literature search.

BACKGROUND: Research on penile cancer (PeCa) is predominantly conducted in countries with centralized treatment of PeCa-patients. In Germany and Austria (G + A), no state-regulated centralization is established, and no information is available on how PeCa-research is organized. METHODS: Current research competence in PeCa was assessed by a 36-item questionnaire sent to all chairholders of urological academic centers in G + A. Based on PubMed records, all scientific PeCa-articles of 2012-2022 from G + A were identified. Current research trends were assessed by dividing the literature search into two periods (P1: 2012-2017, P2: 2018-2022). A bibliometric analysis was supplemented. RESULTS: Response rate of the questionnaire was 75%, a median of 13 (IQR: 9-26) PeCa-patients/center was observed in 2021. Retrospective case series were conducted by 38.9% of participating clinics, while involvement in randomized-controlled trials was stated in 8.3% and in basic/fundamental research in 19.4%. 77.8% declared an interest in future multicenter projects. 205 PeCa-articles were identified [median impact factor: 2.77 (IQR: 0.90-4.37)]. Compared to P1, P2 showed a significant increase in the median annual publication count (29 (IQR: 13-17) vs. 15 (IQR: 19-29), p < 0.001), in multicenter studies (79.1% vs. 63.6%, p = 0.018), and in multinational studies (53% vs. 28.9%, p < 0.001); the proportion of basic/fundamental research articles significantly declined (16.5% vs. 28.9%, p = 0.041). Four of the top-5 institutions publishing PeCa-articles are academic centers. Bibliometric analyses revealed author networks, primary research areas in PeCa, and dominant journals for publications. CONCLUSIONS: Given the lack of centralization in G + A, this analysis highlights the need for research coordination within multicenter PeCa-projects. The decline in basic/fundamental research should be effectively addressed by the allocation of funded research projects.

A proposed framework for the implementation of head and neck cancer treatment at a new cancer center from a radiation oncology perspective.

BACKGROUND: Establishing a new head and neck cancer (HNC) treatment center requires multidisciplinary team management and expertise. To our knowledge, there are no clear recommendations or guidelines in the literature for the commencement of HNC radiation therapy (RT) at a new cancer center. We propose a novel framework outlining the necessary components required to set-up a new radiation therapy HNC treatment. METHODS: We reviewed the infrastructure and methodology in the commencement of HNC radiation therapy in our cancer care center and invited several external, experienced metropolitan head and neck radiation oncologists to develop a novel consensus guideline that may be used by new RT centers to treat HNC. Recommendations were presented to our internal and external staff specialists using a survey questionnaire with ratings utilized to determine consensus using pre-defined thresholds as per the American Society of Clinical Oncology Guidelines Methodology Manual. CONCLUSION: This consensus recommendation aims to improve RT utilization whilst advocating for optimal patient outcomes by presenting a framework for new radiation therapy centers ready to step up and manage the treatment of head and neck cancer patients. We propose these evidence-based consensus guidelines endorsed by external HNC radiation oncologists.

EU legal proposal for genome-edited crops hints at a science-based approach.

The European Commission (EC) recently published a legislative proposal that hints at a science-based approach to the regulation of genome-editing applications in crops in the EU. This would be in line with legislation in an increasing number of countries worldwide, but further science-based advice on implementation will be essential.

Prevent, curtail and mitigate ocular injuries: Proposal for designing an eye injury registry model for the Indian Armed Forces.

This article aims to propose a design for Eye Injury Registry (EIR) model for Indian Armed Forces, to make ophthalmologists and non-ophthalmologists aware about the existence as well as the usefulness of such a registry. This is a perspective study. The EIR model for Armed Forces was designed based on the relevant sources in PubMed, Scopus and Embase including registries of pioneering countries like United States and Canada. A questionnaire based on the model dimensions was developed (Cronbach's alpha>0.7) and filled by 04 senior ophthalmologists in Armed Forces, all of who had a significant experience in dealing with various types of ocular trauma, to give expert opinions, which were then applied to the proposed model to finalize it. In Armed Forces, a registry and reporting on eye injury along with a systematic collection of standard data on eye injuries will help ophthalmologists in the successful prevention. Such a registry and its large database once formed will permit elaborate epidemiologic investigations, highlighting preventable sources of injury, emerging patterns of trauma in our services, and the best possible treatment protocols to be adopted, for successful outcomes. EIR in Armed Forces can help in the collection of eye injury data, thereby improving the quality-of-care and expansion of prevention strategies for ocular injuries. It is a step to make a truly effective data bank, which will be instrumental in combating such preventable ocular injuries and in turn go a very long way in achieving the final goal of preventing up to 90% of such injuries.

Real-world adherence to toxicity management guidelines for immune checkpoint inhibitor-induced diabetes mellitus.

Objective: Immune checkpoint inhibitors(ICIs) have improved survival and are increasingly used for cancer. However, ICIs use may be limited by immune-related adverse events (irAEs), such as ICI-induced diabetes mellitus(ICI-DM). The objective of the present study was to characterize ICI-DM patients and real-world adherence to guidelines. Research design and methods: The present study was a retrospective review of electronic records of ICI-DM patients at the First Affiliated Hospital of Nanjing Medical University between July 2018 and October 2022. Results: 34.8% (8/23)patients monitored blood glucose in every treatment cycle. The proportion of patients with severe diabetic ketoacidosis(DKA) was lower in the tight glycemic monitoring group than the non-tight glycemic monitoring group (16.7% vs. 55.6%, p = 0.049). 78.3%(18/23) patients with hyperglycemia visited a non-endocrinologist first, but 95.7% of patients were then referred to an endocrinologist. Twenty patients were tested for distinguishing the etiology of hyperglycemia and 20% patients with positive glutamic acid decarboxylase antibody(GADA), 55% with C-peptide <3.33pmol/L. High screening rates for other ICI-induced endocrinopathies were observed and half of the patients with ICI-DM developed other endocrine gland irAEs, with the most common being thyroiditis. Moreover, five patients developed non-endocrine serious adverse events(SAEs). Twelve (52.2%) patients were withdrawn from ICI due to ICI-DM. The time to progression of tumor in ICI-DM patients in the continue and interruption group was longer than in the withdrawal group (333.5 ± 82.5 days vs. 183.1 ± 62.4 days, p = 0.161). Only 17.4% of ICI-DM patients were completely managed according to guidelines. Thus, the present study proposed a screening, diagnosis, and management algorithm for ICI-DM in real-world practice. Conclusion: The present study reported the largest number of ICI-DM cases described in a single institute, providing insight into real-world ICI-DM management guideline adherence and highlighting the clinical challenges in ICI-DM management.

PNANet: Probabilistic Two-Stage Detector Using Pyramid Non-Local Attention.

Object detection algorithms require compact structures, reasonable probability interpretability, and strong detection ability for small targets. However, mainstream second-order object detectors lack reasonable probability interpretability, have structural redundancy, and cannot fully utilize information from each branch of the first stage. Non-local attention can improve sensitivity to small targets, but most of them are limited to a single scale. To address these issues, we propose PNANet, a two-stage object detector with a probability interpretable framework. We propose a robust proposal generator as the first stage of the network and use cascade RCNN as the second stage. We also propose a pyramid non-local attention module that breaks the scale constraint and improves overall performance, especially in small target detection. Our algorithm can be used for instance segmentation after adding a simple segmentation head. Testing on COCO and Pascal VOC datasets as well as practical applications demonstrated good results in both object detection and instance segmentation tasks.

The Exacerbation of Chronic Obstructive Pulmonary Disease: Which Symptom is Most Important to Monitor?

Background: GOLD 2023 defines an exacerbation of COPD (ECOPD) by a deterioration of breathlessness at rest (BaR), mucus and cough. The severity of an ECOPD is determined by the degree of BaR, ranging from 0 to 10. However, it is not known which symptom is the most important one to detect early of an ECOPD, and which symptom that predicts future ECOPDs best. Thus, the purpose of the present study was to find out which symptom is the most important one to monitor. Methods: We analysed data on COPD symptoms from the telehealth study The eHealth Diary. Frequent exacerbators (n = 27) were asked to daily monitor BaR and breathlessness at physical activity (BaPA), mucus and cough, employing a digital pen and symptom scales (0-10). Twenty-seven patients with 105 ECOPDs were analysed. The association between symptom development and the occurrence of exacerbations was evaluated using the Andersen-Gill formulation of the Cox proportional hazards model for the analysis of recurrent time-to-event data with time-varying predictors. Results: According to the criteria proposed by GOLD 2023, 42% ECOPDs were mild, 48% were moderate and 5% were severe, while 6% were undefinable. Mucus and cough improved over study time, while BaR and BaPA deteriorated. Mucus appeared earliest, which was the most prominent feature of the average exacerbation, and worsening of mucus increased the risk for a future ECOPD. There was a 58% increase in the risk of exacerbation per unit increase in mucus score. Conclusion: This study suggests that mucus worsening is the most important COPD symptom to monitor to detect ECOPDs early and to predict future risk för ECOPDs. In the present study, we also noticed a pronounced difference between GOLD 2022 and 2023. Hence, GOLD 2023 defined the ECOPD severity much lower than GOLD 2022 did.

A Multi-Stage Faster RCNN-Based iSPLInception for Skin Disease Classification Using Novel Optimization.

Nowadays, skin cancer is considered a serious disorder in which early identification and treatment of the disease are essential to ensure the stability of the patients. Several existing skin cancer detection methods are introduced by employing deep learning (DL) to perform skin disease classification. Convolutional neural networks (CNNs) can classify melanoma skin cancer images. But, it suffers from an overfitting problem. Therefore, to overcome this problem and to classify both benign and malignant tumors efficiently, the multi-stage faster RCNN-based iSPLInception (MFRCNN-iSPLI) method is proposed. Then, the test dataset is used for evaluating the proposed model performance. The faster RCNN is employed directly to perform image classification. This may heavily raise computation time and network complications. So, the iSPLInception model is applied in the multi-stage classification. In this, the iSPLInception model is formulated using the Inception-ResNet design. For candidate box deletion, the prairie dog optimization algorithm is utilized. We have utilized two skin disease datasets, namely, ISIC 2019 Skin lesion image classification and the HAM10000 dataset for conducting experimental results. The methods' accuracy, precision, recall, and F1 score values are calculated, and the results are compared with the existing methods such as CNN, hybrid DL, Inception v3, and VGG19. With 95.82% accuracy, 96.85% precision, 96.52% recall, and 0.95% F1 score values, the output analysis of each measure verified the prediction and classification effectiveness of the method.