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.

Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more severe disease called colorectal cancer. Accurate segmentation of polyps using medical imaging data is essential for effective diagnosis. However, manual segmentation by endoscopists can be time-consuming, error-prone, and expensive, leading to a high rate of missed anomalies. To solve this problem, an automated diagnostic system based on deep learning algorithms is proposed to find polyps. The proposed IRv2-Net model is developed using the UNet architecture with a pre-trained InceptionResNetV2 encoder to extract most features from the input samples. The Test Time Augmentation (TTA) technique, which utilizes the characteristics of the original, horizontal, and vertical flips, is used to gain precise boundary information and multi-scale image features. The performance of numerous state-of-the-art (SOTA) models is compared using several metrics such as accuracy, Dice Similarity Coefficients (DSC), Intersection Over Union (IoU), precision, and recall. The proposed model is tested on the Kvasir-SEG and CVC-ClinicDB datasets, demonstrating superior performance in handling unseen real-time data. It achieves the highest area coverage in the area under the Receiver Operating Characteristic (ROC-AUC) and area under Precision-Recall (AUC-PR) curves. The model exhibits excellent qualitative testing outcomes across different types of polyps, including more oversized, smaller, over-saturated, sessile, or flat polyps, within the same dataset and across different datasets. Our approach can significantly minimize the number of missed rating difficulties. Lastly, a graphical interface is developed for producing the mask in real-time. The findings of this study have potential applications in clinical colonoscopy procedures and can serve based on further research and development.

With the explosive rise of internet usage and the development of web applications across various platforms, ensuring network and system security has become a critical concern. Networks and web services are particularly susceptible to targeted attacks, as hackers and intruders persistently attempt to gain unauthorized access. The integration of artificial intelligence (AI) has emerged as a crucial tool for detecting intrusions and constructing effective Intrusion Detection Systems (IDSs) to counter cyber-attacks and malicious activities. IDSs developed using machine learning (ML) and deep learning (DL) techniques have proven to be highly effective in detecting network attacks, offering machine-centric solutions. Nevertheless, mainstream adoption, confidence and trust in these systems have been greatly impeded by the fact that ML/DL implementations tend to be “black boxes,” and thus lacking human interpretability, transparency, explainability, and logical reasoning in their prediction outputs. This limitation has prompted questions about the responsibility and comprehension of AI-driven intrusion detection systems. In this study, we propose four novel architectures that incorporate Explainable Artificial Intelligence (XAI) techniques to overcome the challenges of limited interpretability in ML/DL based IDSs. We focus on the development of ExplainDTC, SecureForest-RFE, RationaleNet, and CNNShield architectures in network security solutions, and inquiry into their potential to convert the untrustworthy architectures into trustworthy. The models are applied to scan network traffic and identify, and report intrusions based on the traits extracted from the UNSW-NB15 dataset. To explain how a decision is made by the models and to add expansibility at every stage of machine learning pipeline, we integrate multiple XAI methods such as LIME, SHAP, ElI5, and ProtoDash on top of our architectures. The generated explanations provide quantifiable insights into the influential factors and their respective impact on network intrusion predictions.