Computer-aided diagnosis systems: a comparative study of classical machine learning versus deep learning-based approaches

Abstract

The diagnostic phase of the treatment process is essential for patient guidance and follow-up. The accuracy and effectiveness of this phase can determine the life or death of a patient. For the same symptoms, different doctors may come up with different diagnoses whose treatments may, instead of curing a patient, be fatal. Machine learning (ML) brings new solutions to healthcare professionals to save time and optimize the appropriate diagnosis. ML is a data analysis method that automates the creation of analytical models and promotes predictive data. There are several ML models and algorithms that rely on features extracted from, for example, a patient’s medical images to indicate whether a tumor is benign or malignant. The models differ in the way they operate and the method used to extract the discriminative features of the tumor. In this article, we review different ML models for tumor classification and COVID-19 infection to evaluate the different works. The computer-aided diagnosis (CAD) systems, which we referred to as classical, are based on accurate feature identification, usually performed manually or with other ML techniques that are not involved in classification. The deep learning-based CAD systems automatically perform the identification and extraction of discriminative features. The results show that the two types of DAC have quite close performances but the use of one or the other type depends on the datasets. Indeed, manual feature extraction is necessary when the size of the dataset is small; otherwise, deep learning is used.

Artificial Intelligence Techniques and Pedigree Charts in Oncogenetics: Towards an Experimental Multioutput Software System for Digitization and Risk Prediction

“…CAD (Computer-Aided Detection/Diagnosis) systems are digital instruments which have emerged over the last 20 years to cope with problems related to the limits of human perception [13], aiming to analyze and process various types of medical images and produce an automatic or semi-automatic diagnosis [14]. These tools are often combined with AI (in particular with Machine Learning (ML)) in order to achieve the most reliable output possible [15]. CAD systems are increasingly used in the medical practice, above all, in oncology; this is demonstrated by the thousands of papers published in recent years, of which, over a hundred were published in 2023 alone (Table 1).…”