The early, valid prediction of heart problems would minimize life threats and save lives, while lack of prediction and false diagnosis can be fatal. Addressing a single dataset alone to build a machine learning model for the identification of heart problems is not practical because each country and hospital has its own data schema, structure, and quality. On this basis, a generic framework has been built for heart problem diagnosis. This framework is a hybrid framework that employs multiple machine learning and deep learning techniques and votes for the best outcome based on a novel voting technique with the intention to remove bias from the model. The framework contains two consequent layers. The first layer contains simultaneous machine learning models running over a given dataset. The second layer consolidates the outputs of the first layer and classifies them as a second classification layer based on novel voting techniques. Prior to the classification process, the framework selects the top features using a proposed feature selection framework. It starts by filtering the columns using multiple feature selection methods and considers the top common features selected. Results from the proposed framework, with 95.6% accuracy, show its superiority over the single machine learning model, classical stacking technique, and traditional voting technique. The main contribution of this work is to demonstrate how the prediction probabilities of multiple models can be exploited for the purpose of creating another layer for final output; this step neutralizes any model bias. Another experimental contribution is proving the complete pipeline’s ability to be retrained and used for other datasets collected using different measurements and with different distributions.

The coronavirus epidemic has spread to virtually every country on the globe, inflicting enormous health, financial, and emotional devastation, as well as the collapse of healthcare systems in some countries. Any automated COVID detection system that allows for fast detection of the COVID-19 infection might be highly beneficial to the healthcare service and people around the world. Molecular or antigen testing along with radiology X-ray imaging is now utilized in clinics to diagnose COVID-19. Nonetheless, due to a spike in coronavirus and hospital doctors’ overwhelming workload, developing an AI-based auto-COVID detection system with high accuracy has become imperative. On X-ray images, the diagnosis of COVID-19, non-COVID-19 non-COVID viral pneumonia, and other lung opacity can be challenging. This research utilized artificial intelligence (AI) to deliver high-accuracy automated COVID-19 detection from normal chest X-ray images. Further, this study extended to differentiate COVID-19 from normal, lung opacity and non-COVID viral pneumonia images. We have employed three distinct pre-trained models that are Xception, VGG19, and ResNet50 on a benchmark dataset of 21,165 X-ray images. Initially, we formulated the COVID-19 detection problem as a binary classification problem to classify COVID-19 from normal X-ray images and gained 97.5%, 97.5%, and 93.3% accuracy for Xception, VGG19, and ResNet50 respectively. Later we focused on developing an efficient model for multi-class classification and gained an accuracy of 75% for ResNet50, 92% for VGG19, and finally 93% for Xception. Although Xception and VGG19’s performances were identical, Xception proved to be more efficient with its higher precision, recall, and f-1 scores. Finally, we have employed Explainable AI on each of our utilized model which adds interpretability to our study. Furthermore, we have conducted a comprehensive comparison of the model’s explanations and the study revealed that Xception is more precise in indicating the actual features that are responsible for a model’s predictions.This addition of explainable AI will benefit the medical professionals greatly as they will get to visualize how a model makes its prediction and won’t have to trust our developed machine-learning models blindly.

Background/Objectives: Melanoma is a life-threatening skin cancer characterized by aggressive progression and high metastatic potential, making early diagnosis essential for improving patient survival and treatment outcomes. However, accurate automated skin lesion classification remains challenging due to variations in lesion appearance, illumination, image quality, and the presence of artifacts. This study proposes a unified framework for robust multi-class skin cancer classification by integrating preprocessing, lesion segmentation, feature extraction, optimization, and classification within a single end-to-end architecture. Methods: The proposed framework employs an iterative hair artifact removal strategy based on the fusion of Frangi vesselness filtering, Gabor texture filtering, morphological refinement, and Telea inpainting to preserve lesion integrity. A novel dermoscopic lesion segmentation network (CutisNet) is introduced to accurately delineate lesion boundaries. Hybrid representation learning combines deep features extracted using MobileNetV2 with uniquely selected handcrafted descriptors to capture complementary texture, structural, and contextual information. Gray Wolf Optimization is utilized for feature fusion and refinement, while a hybrid GNN–CNN classifier performs robust multi-class skin lesion classification. Results: Extensive experiments conducted on multiple benchmark dermoscopic datasets demonstrate the effectiveness and generalization capability of the proposed framework. The proposed model consistently outperformed existing state-of-the-art methods, achieving a maximum classification accuracy of 96.7% on the PH² dataset while maintaining competitive performance across other benchmark datasets. Conclusions: The proposed framework effectively integrates novel preprocessing, segmentation, hybrid feature representation, feature optimization, and classification strategies to improve the robustness and accuracy of automated skin cancer classification. These results demonstrate its potential to support reliable computer-aided diagnosis and assist clinicians in the early detection of skin cancer.