Revolutionizing Breast Cancer Detection: A Novel Approach with Advanced Image Processing Techniques

The main focus of this study is the improvement of the accuracy and efficiency of breast cancer detection by means of advanced GPU-accelerated image processing techniques and Convolutional Neural Networks (CNNs). Mammograms, considered the gold standard for breast cancer screening, frequently suffer from the problems of noise and low contrast, which sometimes lead to false diagnosis. This method utilizes Laplacian filtering, Contrast Limited Adaptive Histogram Equalization (CLAHE), and unsharp masking to enhance image quality and make tumor regions visible. These techniques are applied to grayscale-converted images, which helps to reduce complexity while retaining critical intensity information. Any process accelerated by a GPU reduces processing time significantly without loss of accuracy; hence, computationally intensive tasks are executed rapidly. . The images are analyzed further using a CNN model trained on improved radiological data for tumor identification and classification. This hybrid approach has utilized traditional techniques of image enhancement along with deep learning for generating robust results by precisely identifying regions of the tumors. Experimental results have shown improvements in accuracy rates for tumor detection along with efficient processing over traditional approaches. Its feasibility as a worthy tool in clinical diagnostics is reinforced by the usage of GPU-based processing and neural networks. Future research aims to refine detection accuracy through advanced CNN architectures and real-time applications in clinical settings.