Authors: Dr.K.ChandraSekhar, Villa Tejaswi, Vadakattu Lahari Malleswari, Chittavadagi Cristin Pratheek, Mandanakka Surya
Abstract: Brain tumours are one of the most serious neurological disorders that can significantly affect human health and quality of life. Early and accurate detection of brain tumours is essential for effective treatment and improved patient survival rates. Magnetic Resonance Imaging (MRI) is widely used by medical professionals to analyse brain structures and detect abnormalities. However, manual examination of MRI scans can be time-consuming and may lead to inconsistent results due to human interpretation. With recent advancements in artificial intelligence, deep learning techniques have shown great potential in assisting medical experts by automatically analysing medical images.This study presents an intelligent brain tumour detection and classification framework based on deep learning and transfer learning techniques. The proposed system utilizes pre-trained convolutional neural network models to extract meaningful features from MRI images and classify them into multiple tumour categories. Several deep learning architectures, including VGG16, InceptionV3, ResNet50, VGG19, InceptionResNetV2, and Xception, are implemented and evaluated for performance comparison. To improve classification accuracy, an ensemble learning approach is also explored by combining the predictions of the best-performing models.In addition to improving prediction accuracy, the system integrates Explainable Artificial Intelligence (XAI) techniques to provide visual explanations of the regions in MRI images that contribute to the model's predictions. This helps increase transparency and reliability, which are important for medical applications.Experimental results demonstrate that the ensemble-based deep learning model achieves higher accuracy compared to individual models while providing reliable tumour classification results. The proposed framework can assist healthcare professionals in detecting brain tumours more efficiently and may contribute to faster diagnosis and better treatment planning in clinical environments.
DOI: http://doi.org/10.61137/ijsret.vol.12.issue2.172
