Multiple Diseases Detection

Abstract

The rapid advancement of deep learning and computer vision has enabled development of intelligent systems capable of detecting multiple diseases simultaneously from medical imaging data. This project proposes a Multi Disease Detection System integrating Brain Tumor detection from MRI scans and Pneumonia detection from chest X-ray images into a unified deep learning framework. Brain tumors one of the most life-threatening forms of cancer are detected from MRI images using a Convolutional Neural Network(CNN) Pneumonia, a leading cause of mortality worldwide particularly in children under five, is detected from chest X-ray images using transfer learning models including VGG16 and ResNet50 fine-tuned on the Kaggle Chest X-Ray Pneumonia dataset. The proposed system employs a dual-model architecture sharing a common preprocessing pipeline. Both models are trained on established benchmark datasets BraTS for brain tumors and the Kaggle Chest X-Ray dataset for pneumonia and evaluated using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The combined system achieves high accuracy in both diagnostic tasks, offering a cost- effective, non-invasive, automated decision-support tool suitable for clinical environments, especially in resource-limited healthcare settings where specialist availability is scarce.