As the leading cause of dementia worldwide, Alzheimer's disease afflicts millions, with progressively impaired abilities to carry out daily activities or communicate and even recognize faces. Although the cause behind lupus is not fully understood, it probably reflects lifestyle choices and environmental factors as well as genetic propensity. The largest obstacles in the diagnosis of these diseases are their often subtle early manifestations and absence of sensitive detection paradigms. Deep-learning algorithms first came to the forefront of medical imaging just a few years ago and were celebrated as sophisticated diagnostic aids, able to spot subtle signs in scans usually hidden from human eyes. We are benefitting from the use of these state-of-the-art algorithms to improve Alzheimer's detection, with one of the largest MRI datasets available today (more than 86,000 images) being used to train our model. In view of this vast data set, it was appreciably combined one to be accurate-centric diagnostic tool. The performance of our novel deep learning model is strong and provided state-of-the-art validation accuracy (99.63%), surpassing existing models These figures highlight the great promise of our model as a verifiable method for detecting early-stage Alzheimer's disease - a significant concern in controlling and managing disease progression. Our research truly is a major step forward in the field of Alzheimer's disease diagnosis by employing cutting-edge deep learning techniques. Early diagnosis allows for better treatment and lower disease burden that can prevent morbidity, mortality and even change many patient outcomes. This is a considerable improvement toward diagnosing Alzheimer's disease with the help of artificial intelligence and presents an expectation for more exact and timely finding.

The resurgence of deep learning has improved computer vision by increasing its applicability and scalability for challenges in the real world. Specifically, utilizing attention in computer vision tasks has improved the performance of models to a superior level. Today we need medical diagnostic tools to tackle the immediate needs of the population suffering from Cancer and COVID-19. Thus, an end-to-end screening is tedious and needs validation expertise. But, with the current deep learning methods, it is quite possible to provide a diagnostic tool that can assist doctors and patients with their immediate needs. So, to tackle these real-world problems, we have proposed a method that is implied on 3 diverse standard datasets in the field of medical imagery, which are Skin Lesions, Brain tumors, and COVID-19 classification. To justify the model’s performance, the authors have experimented on 5 diverse data sets ranging from binary class to multi-class. The experimentation has shown that the proposed “streamlined-attention module” is not only capable of producing superior performance in fine-grained visual recognition but also bio-medical imagery. To further justify, we have illustrated Grad-Cam heat map visualizations to the model and show that it can extract the detailed features with proportionate attention. Our results have proved that our methods excel in their performance compared to that of existing methods. It has achieved state-of-the-art accuracy scores on COVID-19 and HAM10000 datasets with a well-guided explainable result. This work represents a significant advancement in the field of medical image processing with clear results, and the authors anticipate that the suggested method will prove to be a useful tool for medical professionals in the detection and identification of diseases like Covid-19 and cancer. This paper provided the best accuracy for COVID-19 multiclass (94.75 ± 1.07) and HAM10000 (94.31 ± 0.91).

Recently, multi-heart disease diagnosis has become a popular research domain. Yet the traditional systems were ineffective due to the limited signal-processing approaches. Therefore, a deep generalized extreme value mish convolutional neural network with L1,2 regularization (DGMC-L1,2)-based multi-heart disease classification is implemented in this paper using electrocardiogram (ECG) and phonocardiogram (PCG). Primarily, the ECG and PCG are gathered and then preprocessed. After preprocessing, the peak waves are identified using the discrete cross-wavelet transform (DCWT). In the same way, the Osborn wave (OW) and opening snap (OS) are predicted from the ECG and PCG, respectively, using fuzzy exponential-decay-riseton inference system (FEDRIS), followed by wave interval segmentation. Furthermore, the dendrogram with a scatter plot is generated, and then the features are extracted. In addition, the peak localized ECG and PCG are fused together. Subsequently, the subsequent derivatives are estimated, and then a bivariate correlation matrix is created. Then, the feature extraction is done, followed by dimensionality reduction. Here, the dimensionalities of the features are reduced using log-linear scaling2—Principal Component Analysis (LS2-PCA) and then fed into the proposed DGMC-L1,2, which effectively predicts multi-heart diseases. Thus, the experimental results proved that the proposed work had high supremacy with an accuracy of 98.99%.