A dl approach to classify brain tumor detection using radiological images

The advancement of medical imaging has revolutionized the early detection and diagnosis of brain tumors, significantly improving patient outcomes. This project presents a deep learning-based approach for brain tumor classification using radiological images, with a focus on four tumor types: glioma, meningioma, no tumor, and pituitary tumors. The system employs a combination of Convolutional Neural Networks (CNN), MobileNet, Quantum Support Vector Machine (QSVM), ResNet, and InceptionNet algorithms to achieve accurate tumor classification. Each model is leveraged for its unique strengths: CNN for feature extraction, MobileNet for lightweight and efficient computation, QSVM for handling complex feature spaces, ResNet for deep network learning, and InceptionNet for capturing multi-scale information in the images. The models are trained on a dataset of radiological images, enabling them to learn and identify distinct features that differentiate tumor types. The project aims to provide an automated, reliable, and efficient tool for brain tumor detection that could assist healthcare professionals in early diagnosis, reducing the time and cost associated with manual image analysis. The performance of each model is evaluated using standard metrics such as accuracy, precision, recall, and F1-score, with a focus on achieving a balance between classification performance and computational efficiency. The ultimate goal is to enhance the clinical workflow by integrating deep learning techniques for brain tumor detection, contributing to timely and accurate medical decision-making.

Keywords: Deep Learning, Brain Tumor Detection, CNN, MobileNet, QSVM, ResNet, InceptionNet, Radiological Images, Tumor Classification, Medical Imaging, Machine Learning, Healthcare, Artificial Intelligence.