Breast Cancer Classification Using: Pixel Interpolation

TL;DR

This paper presents a novel image interpolation method to classify breast tumors as benign or malignant based on their shape irregularity in mammogram images, aiding faster diagnosis.

Contribution

It introduces a pixel interpolation technique that enhances tumor boundary analysis for improved breast cancer classification in mammograms.

Findings

System classifies tumors with high accuracy

Faster processing time for radiologists

Effective boundary smoothing improves malignancy detection

Abstract

Image Processing represents the backbone research area within engineering and computer science specialization. It is promptly growing technologies today, and its applications founded in various aspects of biomedical fields especially in cancer disease. Breast cancer is considered the fatal one of all cancer types according to recent statistics all over the world. It is the most commonly cancer in women and the second reason of cancer death between females. About 23% of the total cancer cases in both developing and developed countries. In this work, an interpolation process was used to classify the breast cancer into main types, benign and malignant. This scheme dependent on the morphologic spectrum of mammographic masses. Malignant tumors had irregular shape percent higher than the benign tumors. By this way the boundary of the tumor will be interpolated by additional pixels to make the boundary smoothen as possible, these needed pixels is proportional with irregularity shape of the tumor, so that the increasing in interpolated pixels meaning the tumor goes toward the malignant case. The proposed system is implemented using MATLAB programming and tested over several images taken from the Mammogram Image Analysis Society (MIAS) image database. The MIAS offers a regular classification for mammographic studies. The system works faster so that any radiologist can take a clear decision about the appearance of calcifications by visual inspection.