Jul 2026· International Conference Computing Methodologies and Communication· pp. 1339-1345· 0 citations· 12 references
Abstract
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.
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
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.
D. S. Rani, Anjaiah Adepu· 2026 4th International Confe...· 0 citations
Problems with uncontrolled cell growth in the human body are a major cause of cancer, like brain cancer, which occurs in the brain or the nervous system. Tumours are categorised as benign or malignant and are defined by cellular activity and structure. Cancerous tumours are invasive and might require surgical removal, so early and accurate diagnosis is crucial. A radiologist-based diagnosis manually is time-consuming and error-prone, especially in denseness regions. This work aims to solve this problem through the design and application of computer-aided design (CAD) approaches for tumour detection and classification of MRI data in the brain. The method starts from the pre-processing of images by Anisotropic Diffusion with Median Filtering (ADWMF) for noise removal and for the enhancement of the tumour edge. For the segmentation, we employ the IHMFKC algorithm. Feature extraction is performed based on GLCM and FOS. In total, five classifiers (i.e., YOLOv3, Faster R-CNN, ResNet-50, PNNsf3, KNN) are realised via IHMFKC. As a result, IHMFKC embedded with PNN and YOLOv3 obtained classification accuracies of 96.1% and 95.71%, respectively. The experimental results confirm that the proposed method can achieve high performance, accuracy, and reliable results, and has the potential capacity to assist doctors in distinguishing between benign and malignant tumours, helping them make the best decision to save victims of brain cancer.
A. Dudhe, P. Burade· International journal of 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
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
: Recent developments in artificial intelligence (AI) and machine learning (ML) have significantly advanced the detection and classification of brain tumors. Most of these breakthroughs can be traced back to new algorithms, such as deep neural networks, transfer learning, hybrid classifiers, Bayesian models, and edge detection methods. These algorithms have virtually transformed our understanding of cancer, primarily through the analysis of MRI and CT images, offering precision, stability, and speed. The goal of this study is to review the new algorithms used for detecting and/or classifying brain tumors with the help of some example methods and papers. Deep neural networks and transfer learning enable straightforward modifications to the input domain, addressing the complex issues they solve in region-based pattern recognition (e.g., MRI and CT). Hybrid classifiers improve a system's performance by merging two or more algorithms that can individually deliver optimal results. The ability to capture uncertainty is one area in which Bayesian modelling excels. Consequently, Bayesian models will have wider clinical use than edge detection techniques, which can accurately identify tumor margins but are hindered by issues such as an inadequate number of annotated datasets, difficulty in developing computational algorithms, and reduced generalization beyond the condition in which the model was trained. This article discusses the limitations of the current models in terms of their clinical applicability. This article covers changes over time in this field as well as the various means of combining those changes to harness the full power of artificial intelligence. AI can transform clinical decision-making in numerous ways, the study points out, including the imperative to implement decisions in real-time, improve the interpretability of models, and create hybrid models that merge the characteristics of different model types and their applications. Advances in imaging-related research, besides being great for medical practice, signal a significant facet of the study of the future, especially in the areas of detection and classification of brain tumors.
Kavita Ghuge, Sandeep Musale, Supriya Mangale· Proceedings of the 1st Inter...· 0 citations
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