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crossrefSensors2025-04-26Cited by 38

Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review

Rim Missaoui, Wided Hechkel, Wajdi Saadaoui, Abdelhamid Helali, Marco Leo

A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. Due to its superior contrast resolution and safety compared to other imaging methods, MRI is highlighted as the preferred imaging modality for brain tumors. The challenges related to brain tumor analysis in different processes including detection, segmentation, classification, and survival prediction are addressed along with how ML/DL approaches significantly improve these steps. We systematically analyzed 107 studies (2018–2024) employing ML, DL, and hybrid models across publicly available datasets such as BraTS, TCIA, and Figshare. In the light of recent developments in brain tumor analysis, many algorithms have been proposed to accurately obtain ontological characteristics of tumors, enhancing diagnostic precision and personalized therapeutic strategies.

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crossrefSensors2023-07-11Cited by 15

Adversarial-Aware Deep Learning System Based on a Secondary Classical Machine Learning Verification Approach

Mohammed Alkhowaiter, Hisham Kholidy, Mnassar A. Alyami, Abdulmajeed Alghamdi, Cliff Zou

Deep learning models have been used in creating various effective image classification applications. However, they are vulnerable to adversarial attacks that seek to misguide the models into predicting incorrect classes. Our study of major adversarial attack models shows that the…

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crossrefSensors2024-02-08Cited by 17

Gait Characterization in Duchenne Muscular Dystrophy (DMD) Using a Single-Sensor Accelerometer: Classical Machine Learning and Deep Learning Approaches

Albara Ah Ramli, Xin Liu, Kelly Berndt, Erica Goude, Jiahui Hou, Lynea B. Kaethler, et al.

Differences in gait patterns of children with Duchenne muscular dystrophy (DMD) and typically developing (TD) peers are visible to the eye, but quantifications of those differences outside of the gait laboratory have been elusive. In this work, we measured vertical, mediolateral,…

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crossrefSensors2024-11-27Cited by 7

Ultrasound Versus Elastography in the Diagnosis of Hepatic Steatosis: Evaluation of Traditional Machine Learning Versus Deep Learning

Rodrigo Marques, Jaime Santos, Alexandra André, José Silva

The prevalence of fatty liver disease is on the rise, posing a significant global health concern. If left untreated, it can progress into more serious liver diseases. Therefore, accurately diagnosing the condition at an early stage is essential for more effective intervention and…

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crossrefSensors2024-08-16Cited by 10

Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep Learning Methods Using Pressure Insoles

Amal Kammoun, Philippe Ravier, Olivier Buttelli

The three Ground Reaction Force (GRF) components can be estimated using pressure insole sensors. In this paper, we compare the accuracy of estimating GRF components for both feet using six methods: three Deep Learning (DL) methods (Artificial Neural Network, Long Short-Term Memor…

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crossrefSensors2024-07-04Cited by 47

Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis

João N. D. Fernandes, Vitor E. M. Cardoso, Alberto Comesaña-Campos, Alberto Pinheira

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results…

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crossrefSensors2021-11-30Cited by 36

Vision-Based Driver’s Cognitive Load Classification Considering Eye Movement Using Machine Learning and Deep Learning

Hamidur Rahman, Mobyen Uddin Ahmed, Shaibal Barua, Peter Funk, Shahina Begum

Due to the advancement of science and technology, modern cars are highly technical, more activity occurs inside the car and driving is faster; however, statistics show that the number of road fatalities have increased in recent years because of drivers’ unsafe behaviors. Therefor…

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