AI-Based Multi-Task Medical Image Analyzer for Automated Disease Detection across X-Ray, CT, and MRI Modalities

The increasing adoption of medical imaging tech- nologies has significantly amplified the need for intelligent, efficient, and automated diagnostic systems to assist healthcare professionals. Manual interpretation of radiological images is often constrained by expert availability, time limitations, and growing patient demand. This paper introduces an AI-based multitask medical image analyzer capable of identifying disease conditions from X-ray, CT, and MRI images. The proposed framework supports disease detection for pneumonia, COVID-19, and brain tumors by employing specialized deep learning models tailored to each modality and diagnostic task. Architectures such as DenseNet121, VGG16, and EfficientNet-B0 are utilized to optimize performance across different imaging scenarios. An automated routing mechanism directs each input image to the appropriate pretrained model based on disease and modality selection. Image preprocessing methods, including contrast en- hancement, histogram normalization, and data augmentation, are applied to strengthen model generalization. Experimental evaluation demonstrates consistent and reliable performance across all datasets, highlighting the effectiveness of the proposed unified yet disease-specific diagnostic framework for clinical decision support and large-scale screening applications.