Self-supervised learning (SSL) is a potential deep learning (DL) technique that uses massive volumes of unlabeled data to train neural networks. SSL techniques have evolved in response to the poor classification performance of conventional and even modern machine learning (ML) and DL models of enormous unlabeled data produced periodically in different disciplines. However, the literature does not fully address SSL’s practicalities and workabilities necessary for industrial engineering and medicine. Accordingly, this thorough review is administered to identify these prominent possibilities for prediction, focusing on industrial and medical fields. This extensive survey, with its pivotal outcomes, could support industrial engineers and medical personnel in efficiently predicting machinery faults and patients’ ailments without referring to traditional numerical models that require massive computational budgets, time, storage, and effort for data annotation. Additionally, the review’s numerous addressed ideas could encourage industry and healthcare actors to take SSL principles into an agile application to achieve precise maintenance prognostics and illness diagnosis with remarkable levels of accuracy and feasibility, simulating functional human thinking and cognition without compromising prediction efficacy.

The adoption of deep learning (DL) and machine learning (ML) has surged in recent years because of their imperative practicalities in different disciplines. Among these feasible workabilities are the noteworthy contributions of ML and DL, especially ant colony optimization (ACO) and whale optimization algorithm (WOA) ameliorated with neural networks (NNs) to identify specific categories of skin lesion disorders (SLD) precisely, supporting even high-experienced healthcare providers (HCPs) in performing flexible medical diagnoses, since historical patient databases would not necessarily help diagnose other patient situations. Unfortunately, there is a shortage of rich investigations respecting the contributory influences of ACO and WOA in the SLD classification, owing to the recent adoption of ML and DL in the medical field. Accordingly, a comprehensive review is conducted to shed light on relevant ACO and WOA functionalities for enhanced SLD identification. It is hoped, relying on the overview findings, that clinical practitioners and low-experienced or talented HCPs could benefit in categorizing the most proper therapeutical procedures for their patients by referring to a collection of abundant practicalities of those two models in the medical context, particularly (a) time, cost, and effort savings, and (b) upgraded accuracy, reliability, and performance compared with manual medical inspection mechanisms that repeatedly fail to correctly diagnose all patients.

A brain tumor is essentially a collection of aberrant tissues, so it is crucial to classify tumors of the brain using MRI before beginning therapy. Tumor segmentation and classification from brain MRI scans using machine learning techniques are widely recognized as challenging and important tasks. The potential applications of machine learning in diagnostics, preoperative planning, and postoperative evaluations are substantial. Accurate determination of the tumor’s location on a brain MRI is of paramount importance. The advancement of precise machine learning classifiers and other technologies will enable doctors to detect malignancies without requiring invasive procedures on patients. Pre-processing, skull stripping, and tumor segmentation are the steps involved in detecting a brain tumor and measurement (size and form). After a certain period, CNN models get overfitted because of the large number of training images used to train them. That is why this study uses deep CNN to transfer learning. CNN-based Relu architecture and SVM with fused retrieved features via HOG and LPB are used to classify brain MRI tumors (glioma or meningioma). The method’s efficacy is measured in terms of precision, recall, F-measure, and accuracy. This study showed that the accuracy of the SVM with combined LBP with HOG is 97%, and the deep CNN is 98%.

Diyabetik retinopati hastalığı dünya çapında milyonlarca insanı etkilemektedir. Diyabetik hastalığın bir komplikasyonu olarak kabul edilir ve göz görüşünü etkileyebilir. Hekimler bu hastalığı tıbbi göz muayenesi ile tespit edebilirler. Nihai kararı vermek için birçok görüntünün işlenmesi gerekir. Neyse ki, bilgisayar destekli karar destek sistemleri, doktorların daha az çaba ve zaman harcayarak doğru kararlar vermelerine yardımcı olabilir. Bu çalışmada, güncel diyabetik retinopati bilgisayar destekli sistemlerin bir derlemesi sunulmaktadır. Çalışma, diyabetik retinopati tespiti için makine öğrenimi veya derin öğrenme yaklaşımlarının kullanıldığı çalışmaları içermektedir. Bu makale, önerilen metodoloji, kullanılan veri seti, elde edilen sonuçlar ve değerlendirme açısından önceki tüm çalışmaları karşılaştırmaktadır. Çalışma ayrıca mevcut diyabetik retinopati veri setlerini de karşılaştırdı. Sonuç olarak, derin öğrenmeye dayalı yöntemlerin en iyi performansı gösterdiğini gördük. Ayrıca, diyabetik retinopati evrelerinin kategorik sınıflandırması, hastalık tespitinde ikili sınıflandırma yapmaktan daha iyiydi. Bu çalışma, araştırmacıların gelecekteki çalışmalarında en iyi metodolojileri ve veri kümelerini seçmelerine yardımcı olur.