Over the past two decades, computer-aided detection or diagnosis has emerged as a highly promising field of research. Its primary goal is to enhance the diagnostic and treatment procedures for radiologists and clinicians in medical image analysis. With the help of big data and advanced Artificial Intelligence (AI) technologies, i.e., machine learning and deep learning algorithms, assist in making our healthcare system more convenient, active, efficient, and personalized. The primary goal of the literature survey study is to present a thorough overview of the most important developments related to Computer-Aided Diagnosis (CAD) systems in medical imaging. This survey holds considerable importance for researchers and professionals in both medical and computer science. Several reviews regarding specific facets of CAD in medical imaging already exist. Nevertheless, the main emphasis of this paper is on covering the complete range of CAD systems’ capabilities in medical imaging. This review article introduces the background concept used for typical CAD systems in medical imaging by outlining and comparing several methods frequently employed in recent studies. The article also offers a comprehensive and well-structured survey of CAD in medicine, drawing from a meticulous selection of relevant publications. Moreover, it describes the process of handling medical images and introduces state-of-the-art AI-based CAD technologies in medical imaging, along with the future directions of CAD. This study indicates that deep learning algorithms are the most effective way to diagnose or detect diseases.

Phishing involves manipulating individuals into revealing private data, e.g., user IDs, bank details, and passwords. The observed surge in fraud is related to increased deception, impersonation, and advanced online attacks. Thus, effective phishing detection methods are required to mitigate escalating global phishing threats. Existing methods (e.g., heuristics-based, signature-based, and visual similarity-based methods) attempt to detect phishing sites, and machine learning (ML) and deep learning (DL) methods are effective in the cybersecurity context in terms of learning from data, offering insights, and forecasting. However, independent ML algorithms are limited when handling complex data, and DL techniques surpass traditional ML methods in terms of performance but require more data and time. This paper introduces ``EnLeM'',  an ensemble learning model that yields excellent precision, i.e., 97.06\% and 96.36\% for before and after feature selection, respectively, compared to individual ML/DL methods. To address computational efficiency, we employ univariate feature selection. Compared to DL models, this approach yielded promising results (10.01 s for prefeature selection and 8.72 s for post-feature selection). The proposed model was assessed against five conventional ML-based classifiers and two DL-based counterparts to evaluate pre/post feature selection and execution times. The experimental results demonstrate EnLeM’s outstanding, scalable, and consistent performance.