Advancements in Brain Tumour Analysis: A Review of Machine Learning, Deep Learning, Image Processing, and Explainable AI Techniques

S. Venu Gopal () and C. H. Kavitha ()
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S. Venu Gopal: JNTUK
C. H. Kavitha: Seshadri Rao Gudlavalleru Engineering College

SN Operations Research Forum, 2025, vol. 6, issue 2, 1-31

Abstract: Abstract Segmenting and detecting Brain Tumours (BTs) stands a substantial task in medical image examination. A tumour arises from the rapid and uncontrolled proliferation of cells in the brain. The primary objective of brain tumour separation is to accurately outline the regions affected by the tumour. Nowadays, Deep Learning (DL) approaches have revealed notable efficacy in addressing diverse mainframe vision challenges with image sorting, target recognition, and semantic segmentation. Numerous deep learning techniques have been employed for BT segmentation and categorization, demonstrating promising outcomes and integration of explainable AIs makes an impact on brain tumour analysis. Leveraging the advancements in modern technologies, this survey presents a complete examination of recently established atlas and statistical-based deep learning methods for BT separation and categorization. The survey encompasses an analysis of over 105 standard research papers, exploring into various technical aspects like different segmentation techniques, different classification techniques, dataset contribution, and the performances. Additionally, the survey offers insightful discussions on possible solutions for future expansion in this field.

Keywords: Atlas-based techniques; Statistical-based techniques; Brain tumour classification; Brain tumour segmentation; Magnetic resonance images; Explainable AIs (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s43069-025-00455-8

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