Internet of Things (IoT) technology has recently been applied in healthcare systems as an Internet of Medical Things (IoMT) to collect sensor information for the diagnosis and prognosis of heart disease. The main objective of the proposed research is to classify data and predict heart disease using medical data and medical images. The proposed model is a medical data classification and prediction model that operates in two stages. If the result from the first stage is efficient in predicting heart disease, there is no need for stage two. In the first stage, data gathered from medical sensors affixed to the patient’s body were classified; then, in stage two, echocardiogram image classification was performed for heart disease prediction. A hybrid linear discriminant analysis with the modified ant lion optimization (HLDA-MALO) technique was used for sensor data classification, while a hybrid Faster R-CNN with SE-ResNet-101 modelwass used for echocardiogram image classification. Both classification methods were carried out, and the classification findings were consolidated and validated to predict heart disease. The HLDA-MALO method obtained 96.85% accuracy in detecting normal sensor data, and 98.31% accuracy in detecting abnormal sensor data. The proposed hybrid Faster R-CNN with SE-ResNeXt-101 transfer learning model performed better in classifying echocardiogram images, with 98.06% precision, 98.95% recall, 96.32% specificity, a 99.02% F-score, and maximum accuracy of 99.15%.

One of the major shortcomings of existing image dehazing algorithms is in estimating scene transmittance, which has assumed many items in the existing algorithms. One key assumption has been pixel uniformity and smoothness. In this paper, we propose to solve the dehazing problem using a combination of single-pass CNN with graph cut algorithms. It considers the transmittance based on differential pixel-based variance, local and global patches and energy functions to improve the transmission map. The proposed algorithm was tested on different images and evaluated based on various evaluation metrics. Our results show more details when compared to four existing benchmark enhancement methods. The proposed method has one major drawback: the over-bright areas tend to lose some features in the final image.

Breast cancer (BrCa) is the most common disease in women worldwide. Classifying the BrCa image is extremely important for finding BrCa at an earlier stage and monitoring BrCa during treatment. The computer‐aided detection methods have been used to interpret BrCa and improve the detection of BrCa during the screening and treatment stages. However, if a new BrCa image is generated for the treatment, it will not classify correctly. The main objective of this research is to classify the BrCa images for newly generated images. The model performs preprocessing, segmentation, feature extraction, and classification. In preprocessing, a hybrid median filtering (HMF) is used to eliminate the noise in the images. The contrast of the images is enhanced using quadrant dynamic histogram equalization (QDHE). Then, ROI segmentation is performed using the USE‐Net deep learning model. The CaffeNet model is used for feature extraction on the segmented images, and finally, classification is made using the improved random forest (IRF) with extreme gradient boosting (XGB). The model obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specificity, 98.96% precision, and 98.70% f1‐score for ultrasound images. The model gives 98.31% accuracy, 99.29% sensitivity, 90.20% specificity, 98.82% precision, and 99.05% f1‐score for mammogram images.

Due to the limited light penetration in underwater environments, sonar equipment plays a crucial role in various commercial and military operations. However, underwater images often suffer from degradation due to scattering and absorption phenomena, resulting in poor visibility of submerged objects. To address this challenge, image enhancement techniques are essential for enhancing the appearance and visibility of underwater objects. This research proposes a novel approach called HLAST-ACNet, which combines the advantages of a hybrid Local Acuity Swin Transformer and an Adapted Coat-Net for Underwater Object Detection (UOD). The HLASwin-T-ACoat-Net leverages Contrast Limited Adaptive Histogram Equalization (CLAHE) to increase the quality of images. Additionally, it incorporates a path aggregation network to integrate deep and shallow feature maps and utilizes online complicated example mining to improve training efficiency. Furthermore, the algorithm improves Region of Interest (ROI) pooling by introducing ROI alignment, which mitigates quantization errors and enhances object detection accuracy. Compared to existing algorithms, the algorithms based on HLASTACNet demonstrate significant improvements in the URPC2018 and OUC datasets, achieving precision rates of 91.25% and 92.36%, respectively. The research model has a higher computational complexity than four existing methods, as evidenced by its GFLOPs, per-image processing time with a speed of 20ms, and the FPS measures for average processed frames per second reaching 2.28s. The research model effectively addressed the challenges and false detection with varying sizes of objects in complicated underwater environments.

Alzheimer’s disease (AD) is an illness affecting the neurological system in people commonly aged 65 years and older. It is one of the leading causes of dementia and, subsequently, the cause of death as it gradually affects and destroys brain cells. In recent years, the detection of AD has been examined in ways to mitigate its impacts while considering early detection through computer-aided diagnosis (CAD) tools. In this study, we developed deep learning models that focus on early detection and classifying each case, non-demented, moderate-demented, mild-demented, and very-mild-demented, accordingly through transfer learning (TL); an AlexNet, ResNet-50, GoogleNet (InceptionV3), and SqueezeNet by utilizing magnetic resonance images (MRI) and the use of image augmentation. The acquired images, a total of 12,800 images and four classifications, had to go through a pre-processing phase to be balanced and fit the criteria of each model. Each of these proposed models split the data into 80% training and 20% testing. AlexNet performed an average accuracy of 98.05%, GoogleNet (InceptionV3) performed an average accuracy of 97.80%, and ResNet-50 had an average performing accuracy of 91.11%. The transfer learning approach assists when there is not adequate data to train a network from the start, which aids in tackling one of the major challenges faced when working with deep learning.