Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 1696-1703· 0 citations· 30 references
Abstract
The progress in medical imaging technology, including Magnetic Resonance Imaging (MRI), has greatly aided in the prompt identification and diagnosis of brain cancers. This study article provides a comprehensive examination of the use of machine learning methods to evaluate the seriousness of brain tumors using MRI images. The project seeks to assess the latest approaches, problems, and prospects in using machine learning algorithms to improve the precision and effectiveness of tumor severity diagnosis. The review involves a comprehensive examination of several machine learning techniques, encompassing both conventional methods and current breakthroughs like deep learning models, within the specific context of evaluating the severity of brain tumors. The study investigates the use of several strategies for extracting features, preprocessing data, and using classification algorithms to distinguish between benign and malignant tumors. In addition, the research examines the combination of many types of imaging data and the inclusion of clinical information to enhance the overall prediction accuracy. In addition, this study presents a thorough analysis of the current datasets, benchmarking methods, and assessment criteria to offer valuable insights into the dependability and applicability of the suggested models. The article discusses the difficulties related to having a restricted number of annotated datasets, the comprehensibility of intricate models, and the moral concerns when implementing machine learning in healthcare environments. The survey ends with a discussion on possible future research approaches, highlighting the need for joint endeavors among medical practitioners, image analysts, and machine learning specialists to create strong and practical models for clinical use. The results of this study are anticipated to aid in the progress of medical image analysis and promote the creation of more precise and dependable methods for identifying and describing brain cancers.
One of the most dangerous neurological conditions is brain tumors, and better patient survival and efficient treatment planning depend on an early and precise diagnosis. The most popular imaging method for identifying brain tumors is magnetic resonance imaging (MRI), which offers a thorough image of brain structures without subjecting patients to ionizing radiation. Automated brain tumor identification and categorization has been much more accurate and efficient in recent years because to Artificial Intelligence (AI), especially Machine Learning (ML) and Deep Learning (DL). An extensive overview of current deep learning and machine learning techniques for brain tumor detection from MRI images is provided in this review study. It looks at popular preprocessing strategies, feature extraction techniques, classification algorithms, publically accessible datasets, and assessment measures from current research. It looks at popular preprocessing strategies, feature extraction techniques, classification algorithms, publically accessible datasets, and assessment measures from current research. Additionally, the study compares advanced deep learning architectures like Convolutional Neural Networks (CNN), ResNet, U-Net, Vision Transformers (ViT), and hybrid models with conventional machine learning techniques like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN). Additionally, the study highlights the development of this research field by discussing current research trends using demonstrative PRISMA technique, bibliometric analysis, keyword co-occurrence analysis, and dataset analysis. Additionally, the study compares advanced deep learning architectures like Convolutional Neural Networks (CNN), ResNet, U-Net, Vision Transformers (ViT), and hybrid models with conventional machine learning techniques like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (KNN). Additionally, the study highlights the development of this research field by discussing current research trends using demonstrative PRISMA technique, bibliometric analysis, keyword co-occurrence analysis, and dataset analysis.
Vishwa Desai, S. Degadwala· International Journal of Sci...· 0 citations
The recent development in the field of artificial intelligence and digital medical imaging has also disrupted the process of analysis and diagnosis of neurological disorders. The automated examination of brain MRI scans has gained more significance in helping radiologists to diagnose tumors with high accuracy as well as decrease the time and manual work involved in diagnosis. The problem of brain tumor detection and classification is not resolved yet, as each patient has different tumors, and some have different shapes, sizes, and intensity. The proposed project will resolve these problems by implementing deep learning based object detection models to tackle medical image analysis with an automated classifier of brain tumors and their rate of growth with the help of the YOLOv8 deep learning model. Preprocessing of MRI images involves a preprocessing pipeline comprising of resizing and normalization of the aspect ratio to enhance localization of tumors to be used as a bounding box in detecting them. Brain tumors are recognized as four types; glioma, meningioma, pituitary tumor and no tumor (YOLOv8 model). The system provides the results of prediction as the type of tumor, the segmented part of the tumor, and the confidence score. The size of the tumors is estimated by the area identified and the rate of growth is calculated based on the analysis carried out by medical rules. The experimental findings prove that the suggested deep learning model not only offers effective tumor detection and classification but also effective growth evaluation, which makes it appropriate as a clinical decision support system in the field of early diagnosis and regular follow-ups.
M. Parameswari, R. D, S. S et al.· International Conference Com...· 0 citations
The study adds a rigorous benchmarking mechanism and empirical evidence for adopting ResNet50 as a robust model for multi-class brain tumour diagnosis and highlights the power of deep residual learning for solving some of the difficulties in classifying brain MRI, such as inter-class similarity and feature heterogeneity.
Prabha Kumaresan, Xin-Tian Lim· International Journal on Rob...· 0 citations
Recently, the world has observed emerging transformations in several domains due to the crucial developments in computer vision and Artificial Intelligence (AI) such as Machine Learning (ML) and Deep Learning (DL) techniques. This revolution has also inspired the medical sector as it generates a huge range of information. Among various medical domains, these techniques have shown excellence in identifying and categorizing brain tumors using different modality images. Thereby, this research study aims to review the use of AI techniques in brain tumor detection and classification studies by using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. This review covers the insights about the review of several AI techniques for brain tumor detection and classification approaches, concerning complicated tumor types and heterogeneous datasets. This review has also explored imaging modalities used for brain tumor analysis along with the conceptual overview on performance measures considered and experimental platforms considered for the experimentation. This analysis offered promising results to assist future researchers with a comprehensive perspective on recent research trends and analysis of several DL methodologies. Finally, it also discusses the challenges and future research directions on upcoming research challenges and opportunities for brain tumor detection techniques to assist medical professionals.
Sumaiya, Ranjit K N· Genetics and Molecular Resea...· 0 citations
A brain tumor classification system integrated with Explainable Artificial Intelligence (XAI) was developed using MRI images and demonstrated effective classification performance and improved interpretability, making it suitable for automated brain tumor diagnosis.
T. H. Stephen, A. Oke, A. S. Falohun et al.· LAUTECH Journal of Engineeri...· 0 citations
One of the most serious conditions affecting the nervous system is brain tumors, and a patient’s successful treatment depends on a correct diagnosis. A radiologist’s manual processing of MRI images takes a lot of time and can result in some incorrect diagnoses, particularly when the tumor’s boundaries are unclear. In order to address this issue, this paper suggests an automated system for brain tumor detection that combines deep learning algorithms with OpenCV-based image processing methods. First, the MRI pictures are preprocessed using methods for region of interest extraction, noise reduction, and contrast enhancement. A convolutional neural network is then fed the preprocessed images in order to identify and detect tumors. It is anticipated that the suggested system will increase tumor detection accuracy without sacrificing efficiency. The experimental results indicate that the combination of traditional image processing techniques with deep learning algorithms can improve the performance of the system and provide a useful tool for computer-aided medical diagnosis.
I. Harshini, M. Abhinaya, Dr.M.V.V.Siva Prasad· International Conference Com...· 0 citations
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