Detecting colorectal polyps promptly and accurately is crucial in preventing the progression of colorectal cancer. These polyps cause severe conditions in the colon or rectum, presenting a significant diagnostic challenge. Traditional manual detection through medical imaging is not only bulky and prone to errors but also incurs substantial costs, requiring expert endoscopist. Inefficient detection and treatment can lead to critical health complications. Addressing these issues, we extensively employed various configurations of the state-of-the-art YOLOv8 (n-nano, s-small, m-medium, l-large, and x-extra-large) models for effective polyp localization. Complementing this, we proposed a novel TR-SE-Net model for segmentation, integrating Squeeze-and-Excite Networks (SE-Net) to elevate performance and real-time processing capabilities. The Kvasir-SEG dataset is utilized for training and testing models, supplemented by external validation CVC-ClinicDB, PolypGen, ETIS-LaribPolypDB, EDD 2020, and BKAI-IGH to confirm their efficacy in processing unseen, real-time data. This study delves into the interpretability of these models using explainable AI (XAI), such as eigen visualization for localization and heatmap analysis for segmentation. This exploration provides deeper insights into the decision-making processes of the models, thereby enhancing their reliability. Notably, the YOLOv8m model showcased remarkable prediction speed (approximately 16.61 ms) and excelled in precision (0.946), recall (0.771), F1-score (0.85), mAP ₅₀ (0.886), and mAP _50-95 (0.695), catering to diverse clinical scenarios. The TR-SE-Net demonstrated significant improvements in segmentation performances, including DSC (0.8754), F2-score (0.8786), precision (0.9027), recall (0.8879), accuracy (0.9647), competitive mIoU (0.7961), FPS (54), parameters (27.27 million), and flops (10.59 GMac). Furthermore, A graphical Computer Aided Diagnosis (CAD) system developed utilizing both models can substantially reduce the miss rate because segmentation will assist in polyp detection or vice versa if localization fails. Conclusively, integrating these advanced computer-aided methods substantially enhances colonoscopy procedures by mitigating the risks of colorectal cancer.

Lung-related diseases continue to be a leading cause of global mortality. Timely and precise diagnosis is crucial to save lives, but the availability of testing equipment remains a challenge, often coupled with issues of reliability. Recent research has highlighted the potential of Chest X-ray (CXR) images in identifying various lung diseases, including COVID-19, fibrosis, pneumonia, and more. In this comprehensive study, four publicly accessible datasets have been combined to create a robust dataset comprising 6650 CXR images, categorized into seven distinct disease groups. To effectively distinguish between normal and six different lung-related diseases (namely, bacterial pneumonia, COVID-19, fibrosis, lung opacity, tuberculosis, and viral pneumonia), a Deep Learning (DL) architecture called a Multi-Scale Convolutional Neural Network (MS-CNN) is introduced. The model is adapted to classify multiple numbers of lung disease classes, which is considered to be a persistent challenge in the field. While prior studies have demonstrated high accuracy in binary and limited-class scenarios, the proposed framework maintains this accuracy across a diverse range of lung conditions. The innovative model harnesses the power of combining predictions from multiple feature maps at different resolution scales, significantly enhancing disease classification accuracy. The approach aims to shorten testing duration compared to the state-of-the-art models, offering a potential solution toward expediting medical interventions for patients with lung-related diseases and integrating explainable AI (XAI) for enhancing prediction capability. The results demonstrated an impressive accuracy of 96.05%, with average values for precision, recall, F1-score, and AUC at 0.97, 0.95, 0.95, and 0.94, respectively, for the seven-class classification. The model exhibited exceptional performance across multi-class classifications, achieving accuracy rates of 100%, 99.65%, 99.21%, 98.67%, and 97.47% for two, three, four, five, and six-class scenarios, respectively. The novel approach not only surpasses many pre-existing state-of-the-art (SOTA) methodologies but also sets a new standard for the diagnosis of lung-affected diseases using multi-class CXR data. Furthermore, the integration of XAI techniques such as SHAP and Grad-CAM enhanced the transparency and interpretability of the model’s predictions. The findings hold immense promise for accelerating and improving the accuracy and confidence of diagnostic decisions in the field of lung disease identification.

Lung and colon cancers are common types of cancer with significant fatality rates. Early identification considerably improves the odds of survival for those suffering from these diseases. Histopathological image analysis is crucial for detecting cancer by identifying morphological anomalies in tissue samples. Regulations such as the HIPAA and GDPR impose considerable restrictions on the sharing of sensitive patient data, mostly because of privacy concerns. Federated learning (FL) is a promising technique that allows the training of strong models while maintaining data privacy. The use of a federated learning strategy has been suggested in this study to address privacy concerns in cancer categorization. To classify histopathological images of lung and colon cancers, this methodology uses local models with an Inception-V3 backbone. The global model is then updated on the basis of the local weights. The images were obtained from the LC25000 dataset, which consists of five separate classes. Separate analyses were performed for lung cancer, colon cancer, and their combined classification. The implemented model successfully classified lung cancer images into three separate classes with a classification accuracy of 99.867%. The classification of colon cancer images was achieved with 100% accuracy. More significantly, for the lung and colon cancers combined, the accuracy reached an impressive 99.720%. Compared with other current approaches, the proposed framework showed an improved performance. A heatmap, visual saliency map, and GradCAM were generated to pinpoint the crucial areas in the histopathology pictures of the test set where the models focused in particular during cancer class predictions. This approach demonstrates the potential of federated learning to enhance collaborative efforts in automated disease diagnosis through medical image analysis while ensuring patient data privacy.