Deep Learning Model for Detecting the Presence of Neurological Conditions in MRI and CT Scans

An AI-powered MRI/CT analysis solution for medical professionals can significantly enhance diagnostic accuracy and efficiency. By harnessing deep learning algorithms, this solution can minimize human error and accelerate image interpretation. The incorporation of a Convolutional Neural Network (CNN) model empowers the solution to identify abnormalities in MRI/CT images with high consistency and reliability. This solution supports intricate case evaluations and integrates effortlessly with electronic health records, making high-quality diagnostic capabilities available to a wider range of healthcare professionals. The solution is meticulously designed to detect various abnormalities, including tumors, Alzheimer's, and hemorrhages from MRI images, as well as COVID-19 and checking for squamous conditions from CT images. Ultimately, this solution aims to improve patient outcomes through precise and prompt diagnoses, while also serving as an educational resource and contributing to ongoing medical research and innovation. The brain tumor MRI model achieved an accuracy of 87.5% after 100 epochs, while the chest CT scan model attained 95.65% accuracy in just 50 epochs. Brain hemorrhage MRI model accomplished 88.45% accuracy in 30 epochs, and the COVID-19 dataset model reached 78.9% accuracy in 40 epochs. Lastly, the Alzheimer's model obtained an accuracy of 80.70% after 40 epochs.