Radiogenomic classification for MGMT promoter methylation status using multi-omics fused feature space for least invasive diagnosis through mpMRI scans.

Accurate radiogenomic classification of brain tumors is important to improve the standard of diagnosis, prognosis, and treatment planning for patients with glioblastoma. In this study, we propose a novel two-stage MGMT Promoter Methylation Prediction (MGMT-PMP) system that extracts latent features fused with radiomic features predicting the genetic subtype of glioblastoma. A novel fine-tuned deep learning architecture, namely Deep Learning Radiomic Feature Extraction (DLRFE) module, is proposed for latent feature extraction that fuses the quantitative knowledge to the spatial distribution and the size of tumorous structure through radiomic features: (GLCM, HOG, and LBP). The application of the novice rejection algorithm has been found significantly effective in selecting and isolating the negative training instances out of the original dataset. The fused feature vectors are then used for training and testing by k-NN and SVM classifiers. The 2021 RSNA Brain Tumor challenge dataset (BraTS-2021) consists of four structural mpMRIs, viz. fluid-attenuated inversion-recovery, T1-weighted, T1-weighted contrast enhancement, and T2-weighted. We evaluated the classification performance, for the very first time in published form, in terms of measures like accuracy, F1-score, and Matthews correlation coefficient. The Jackknife tenfold cross-validation was used for training and testing BraTS-2021 dataset validation. The highest classification performance is (96.84 ± 0.09)%, (96.08 ± 0.10)%, and (97.44 ± 0.14)% as accuracy, sensitivity, and specificity respectively to detect MGMT methylation status for patients suffering from glioblastoma. Deep learning feature extraction with radiogenomic features, fusing imaging phenotypes and molecular structure, using rejection algorithm has been found to perform outclass capable of detecting MGMT methylation status of glioblastoma patients. The approach relates the genomic variation with radiomic features forming a bridge between two areas of research that may prove useful for clinical treatment planning leading to better outcomes.

Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification.

Biomedical engineering is the application of the principles and problem-solving methods of engineering to biology along with medicine. Computation intelligence is the study of design of intelligent agents which are systems acting perceptively. The computation intelligence paradigm offers more advantages to the enhancement and maintenance of the field of biomedical engineering. Liver cancer is the major reason of mortality worldwide. Earlier-stage diagnosis and treatment might increase the survival rate of liver cancer patients. Manual recognition of the cancer tissue is a time-consuming and difficult task. Hence, a computer-aided diagnosis (CAD) is employed in decision making procedures for accurate diagnosis and effective treatment. In contrast to classical image-dependent "semantic" feature evaluation from human expertise, deep learning techniques could learn feature representation automatically from sample images using convolutional neural network (CNN). This study introduces a Hybrid Rider Optimization with Deep Learning Driven Biomedical Liver Cancer Detection and Classification (HRO-DLBLCC) model. The proposed HRO-DLBLCC model majorly focuses on the identification of liver cancer in the medical images. To do so, the proposed HRO-DLBLCC model employs preprocessing in two stages, namely, Gabor filtering (GF) based noise removal and watershed transform based segmentation. In addition, the proposed HRO-DLBLCC model involves NAdam optimizer with DenseNet-201 based feature extractor to generate an optimal set of feature vectors. Finally, the HRO algorithm with recurrent neural network-long short-term memory (RNN-LSTM) model is applied for liver cancer classification, in which the hyperparameters of the RNN-LSTM model are tuned by the use of HRO algorithm. The HRO-DLBLCC model is experimentally validated and compared with existing models. The experimental results assured the promising performance of the HRO-DLBLCC model over recent approaches.

Fuzzy Cognitive Maps with Bird Swarm Intelligence Optimization-Based Remote Sensing Image Classification.

Remote sensing image (RSI) scene classification has become a hot research topic due to its applicability in different domains such as object recognition, land use classification, image retrieval, and surveillance. During RSI classification process, a class label will be allocated to every scene class based on the semantic details, which is significant in real-time applications such as mineral exploration, forestry, vegetation, weather, and oceanography. Deep learning (DL) approaches, particularly the convolutional neural network (CNN), have shown enhanced outcomes on the RSI classification process owing to the significant aspect of feature learning as well as reasoning. In this aspect, this study develops fuzzy cognitive maps with a bird swarm optimization-based RSI classification (FCMBS-RSIC) model. The proposed FCMBS-RSIC technique inherits the advantages of fuzzy logic (FL) and swarms intelligence (SI) concepts. In order to transform the RSI into a compatible format, preprocessing is carried out. Besides, the features are produced by the use of the RetinaNet model. Besides, a FCM-based classifier is involved to allocate proper class labels to the RSIs and the classification performance can be improved by the design of bird swarm algorithm (BSA). The performance validation of the FCMBS-RSIC technique takes place using benchmark open access datasets, and the experimental results reported the enhanced outcomes of the FCMBS-RSIC technique over its state-of-the-art approaches.

Intelligent Epileptic Seizure Detection and Classification Model Using Optimal Deep Canonical Sparse Autoencoder.

Epileptic seizures are a chronic and persistent neurological illness that mainly affects the human brain. Electroencephalogram (EEG) is considered an effective tool among neurologists to detect various brain disorders, including epilepsy, owing to its advantages, such as its low cost, simplicity, and availability. In order to reduce the severity of epileptic seizures, it is necessary to design effective techniques to identify the disease at an earlier stage. Since the traditional way of diagnosing epileptic seizures is laborious and time-consuming, automated tools using machine learning (ML) and deep learning (DL) models may be useful. This paper presents an intelligent deep canonical sparse autoencoder-based epileptic seizure detection and classification (DCSAE-ESDC) model using EEG signals. The proposed DCSAE-ESDC technique involves two major processes, namely, feature selection and classification. The DCSAE-ESDC technique designs a novel coyote optimization algorithm (COA)-based feature selection technique for the optimal selection of feature subsets. Moreover, the DCSAE-based classifier is derived for the detection and classification of different kinds of epileptic seizures. Finally, the parameter tuning of the DSCAE model takes place via the krill herd algorithm (KHA). The design of the COA-based feature selection and KHA-based parameter tuning shows the novelty of the work. For examining the enhanced classification performance of the DCSAE-ESDC technique, a detailed experimental analysis was conducted using a benchmark epileptic seizure dataset. The comparative results analysis portrayed the better performance of the DCSAE-ESDC technique over existing techniques, with maximum accuracy of 98.67% and 98.73% under binary and multi-classification, respectively.

Improved Bald Eagle Search Optimization with Synergic Deep Learning-Based Classification on Breast Cancer Imaging.

Medical imaging has attracted growing interest in the field of healthcare regarding breast cancer (BC). Globally, BC is a major cause of mortality amongst women. Now, the examination of histopathology images is the medical gold standard for cancer diagnoses. However, the manual process of microscopic inspections is a laborious task, and the results might be misleading as a result of human error occurring. Thus, the computer-aided diagnoses (CAD) system can be utilized for accurately detecting cancer within essential time constraints, as earlier diagnosis is the key to curing cancer. The classification and diagnosis of BC utilizing the deep learning algorithm has gained considerable attention. This article presents a model of an improved bald eagle search optimization with a synergic deep learning mechanism for breast cancer diagnoses using histopathological images (IBESSDL-BCHI). The proposed IBESSDL-BCHI model concentrates on the identification and classification of BC using HIs. To do so, the presented IBESSDL-BCHI model follows an image preprocessing method using a median filtering (MF) technique as a preprocessing step. In addition, feature extraction using a synergic deep learning (SDL) model is carried out, and the hyperparameters related to the SDL mechanism are tuned by the use of the IBES model. Lastly, long short-term memory (LSTM) was utilized to precisely categorize the HIs into two major classes, such as benign and malignant. The performance validation of the IBESSDL-BCHI system was tested utilizing the benchmark dataset, and the results demonstrate that the IBESSDL-BCHI model has shown better general efficiency for BC classification.