In recent years, the volume in globally recognized medical data sets are increasing both with attributes and number of records. Machine learning algorithms aiming to detect and diagnose ischemic heart diseases requires high efficacy and judgment. The state of art Ischemic heart disease data sets presents several issues, including feature selection, sample size, sample imbalance, and lack of magnitude for some characteristics etc. The proposed study is primarily concerned with improving feature selection and reducing the number of features yet giving better decisions. In this study, to pick salient aspects of heart illness, an improved squirrel search optimization algorithm with a meta-heuristic approach is proposed. Comparison study of the proposed ischemic heart disease squirrel search optimization (IHDSSO) model in conjunction with random forest classifier ensures better feature selection and accuracy over 98% with respect to other state-of-the-art optimization algorithms.

Alzheimer’s disease (AD) poses urgent significant challenges for early detection and personalized prognostication. Deep learning (DL) is now regarded as a pivotal technology for extracting subtle imaging and non-imaging biomarkers; yet translating these advances into clinical practice demands a coherent framework. In this review, we first survey input modalities from structural and functional MRI to PET, genetic profiles, and cognitive tests and the key public cohorts that supply multimodal data. We then categorize DL architectures into three complementary pillars: (1) end-to-end classification networks for direct diagnosis; (2) multimodal fusion strategies that integrate heterogeneous biomarkers; and (3) automated segmentation pipelines for precise anatomical delineation. We also examine subtyping algorithms that uncover latent AD phenotypes via clustering and decision-tree models. In order to fill the gap between high-performance DL and real-world adoption, we detail explainable AI methods that render model decisions transparent, and we review performance benchmarks including accuracy, sensitivity/specificity, Dice and Jaccard indices to contextualize efficacy. Finally, we discuss clinical translation, covering prospective validation, workflow integration, and regulatory/privacy considerations, before outlining challenges and future directions such as data heterogeneity, interpretability–accuracy trade-offs, early/preclinical detection, and federated learning. Our roadmap highlights the interdisciplinary collaborations and technical innovations needed to deliver robust, trustworthy, and scalable DL-based tools for Alzheimer’s care.

The localization of the optic disc and fovea is crucial in the automated diagnosis of various retinal diseases. We propose a novel deep learning driven heatmap regression model based on the encoder-decoder architecture for the joint detection of optic disc and fovea centers in color fundus images. To train the regression model, we transform the ground-truth center coordinates of optic disc and fovea of the IDRiD dataset to heatmaps using a 2D-Gaussian equation. The model is capable of pinpointing any single pixel in a vast 2D image space. The model is tested on IDRiD test dataset, Messidor, and G1020 datasets. The model outperforms the state-of-the-art methods on these datasets. The model is very robust and generic, which can be trained and used for the simultaneous localization of multiple landmarks in different medical image datasets. The full implementation code and the trained model with weights (based on Keras) are available for reuse at https://github.com/bhargav-jb/FundusPosNet.

Well-timed prediction and an accurate diagnosis of Ischemic Heart disease (IHD) can reduce the risk of death, whereas an inaccurate diagnosis can prove fatal. So, there is a need to develop an optimal heart disease prediction model to avoid inaccurate ischemic heart disease diagnosis and further treatment. Recently, researchers have developed several deep learning techniques that take input from medical practitioners, automatically find hidden patterns in enormous volumes of data, and predict heart diseases without human intervention. Further, the deep learning model can help doctors to classify the severity of heart disease and choose appropriate treatment accordingly. These deep learning models can be improved to achieve greater accuracy and stability. Creating a hybrid model that combines attention residual learning with a Long Short-Term Memory (LSTM) is one method to prove it. Our suggested Hybrid Residual Attention-Enhanced LSTM (HRAE-LSTM) approach improves accuracy and stability by combining attention residual learning with an LSTM. For evaluating the effectiveness of the proposed HRAE-LSTM model, realistic datasets of 303 instances from the heart disease dataset (UCI), were used. The proposed HRAE-LSTM outperforms existing cardiac disease prediction systems by 97.7 % with the UCI dataset, respectively.

The major contributor to global mortality is cardiovascular disease, posing a formidable challenge to the global healthcare system. Heart disease often develops and progresses without noticeable symptoms, emphasizing the need for earlier detection to prevent severe outcomes. AI models provide tools for accurate diagnosis, data analysis, and predictive diagnosis of people who may be suffering from heart disease. But increasingly, solutions like Explainable AI (XAI) are gaining traction. XAI demystifies the decisions of AI models, making them transparent and understandable to the point where they boost confidence in the context of AI diagnostics by healthcare professionals; the AI then provides support to professionals to decide the best course of action for each patient based on the analysis. XAI techniques such as SHAP, LIME, QLattice and Anchor are popular, and have been used in this study. We analysed a heart disease dataset collected from a multispecialty hospital in India, which is publicly available on Mendeley. We used ML models, including RF, Logistic Regression, KNN, XGBoost, DT and SVM. These models were optimized and stack-ensembled to form the ‘CARDIACX’ model to predict the risk of heart disease. The models were fine-tuned using Grid Search, Random Search, and Bayesian Optimization methods, achieving promising results with Random Forest achieving an AUC = 0.99 and accuracy of 98.5%, outperforming all the other models and demonstrating robust performance on various metrics such as AUC, PR Curve, Log Loss, Jaccard Score, and MCC. This study provides a reliable and transparent framework for early detection of heart disease, providing useful information and enabling patients to receive timely and personalized care.

The growing importance of information security over recent years has prompted development of secure encryption algorithms. Most of the traditional algorithms such as AES and DES are computationally intensive. Hence, in this paper we propose a simple but secure symmetric key encryption algorithm. The image encryption approach makes use of two pseudorandom number generators to encrypt images. Middle Square Algorithm is used to swap the columns of the image followed by the swapping of the rows. This permutation step produces the intermediary cipher image. Subsequently, RC4 algorithm is used to produce a stream of pseudo-random numbers. These numbers are used to substitute the intensity of pixels of the intermediary cipher image, which gives the final encrypted image. Various analysis tests are performed on the quality of the encrypted image. The results of these tests prove that the proposed algorithm is secure and efficient.