A Novel Hybrid Deep Learning Model Enhanced with Explainable AI for Brain Tumor Multi-Classification from MRI Images
Source abstract
Abstract
The life span and quality of a patient are greatly diminished by a brain tumor, a type of cancer. For patients, early diagnosis and effective treatment are very significant in this respect. To assist medical professionals in this difficult and error-prone process and improve both the accuracy and interpretability of the model, this study proposes a new hybrid deep learning model enhanced with explainable artificial intelligence for brain tumor multi-classification from MRI images. It integrates a customized CNN model for feature extraction from images and the optimized XGBoost method with high classification success. It also incorporates Grad-CAM, which makes the black-box structure of the model transparent and the decision-making process more understandable. The proposed model classified four different brain tumors, namely glioma, meningioma, notumor and pituitary, with 99.77% accuracy and demonstrated superior performance when compared with existing methods. The results show that a robust, interpretable and high-performance hybrid classification model has been developed for brain tumor detection.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Brain Tumor Detection and Classification · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Software Engineering Department, Firat University, 23190 Elazig, Turkey
Funding
Scientific Research Projects Coordination Unit of Firat University
References (43)
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