Advancing Brain Lesion Classification in CT Images: A Transfer Learning Approach with Convolutional Neural Networks

This study presents a novel hybrid deep learning approach for precise segmentation and detection of various types of brain lesions in CT images, with a particular emphasis on hemorrhagic strokes.” Convolutional neural networks (CNNs) are used to process CT brain images and perform image recognition in the proposed framework. For the purpose of pre-processing adaptive median filtering is used, which improves the quality of the image, which successfully removes noise. Afterward, the pre-processed image will act as an input to the segmentation block, where it is broken into a number of segments for additional processing. The recommended network uses K-means clustering to improve segmentation accuracy. This technique helps in distinguishing the hemorrhagic area from healthy brain tissue by enhancing the contrast. The experimental results obtained using the CNN Classifier demonstrate precise lesion detection. However, it is important to address the computational speed and the limitation of signal flow in one direction only in feed-forward setups to optimize the overall performance of the proposed method.