  This research presents a machine-learning approach for tumor detection in
medical images using convolutional neural networks (CNNs). The study focuses on
preprocessing techniques to enhance image features relevant to tumor detection,
followed by developing and training a CNN model for accurate classification.
Various image processing techniques, including Gaussian smoothing, bilateral
filtering, and K-means clustering, are employed to preprocess the input images
and highlight tumor regions. The CNN model is trained and evaluated on a
dataset of medical images, with augmentation and data generators utilized to
enhance model generalization. Experimental results demonstrate the
effectiveness of the proposed approach in accurately detecting tumors in
medical images, paving the way for improved diagnostic tools in healthcare.
