Detection of Cancer Cells Using Matlab Image Processing (Otsu's Thresholding)

Early and accurate cancer detection is critical for improving treatment outcomes. Traditional methods rely on manual examination of histopathological images, which can be time-consuming and prone to human error. This study introduces a MATLAB-based system that automates cancer cell detection using advanced image processing techniques. The system enhances diagnostic accuracy by performing preprocessing, segmentation, and classification with minimal manual intervention. Experimental results on histopathological images show improved clarity, precise segmentation, and faster detection compared to conventional methods. The system’s scalability and cost-effectiveness make it a viable solution for large-scale screenings. Future work will explore the integration of deep learning models to further refine detection accuracy and efficiency. Keywords-Cancer Detection, MATLAB-Based Diagnosis, Automated Image Analysis, Histopathological Image Processing, Feature Extraction, Medical Image Segmentation, AI in Cancer Screening.