Medmathematica: advancing cancer segmentation through mathematical modeling - Badarul Azam, Akmal Shafiq; Jumaat, Abdul Kadir; Ibrahim, Shafaf; Wondi, Mohd Hafizz (Universiti Teknologi MARA Cawangan Johor Kampus Pasir Gudang, 2025)
MedMathematica is an innovative computer-aided detection (CAD) system designed to improve the accuracy and efficiency of cancer segmentation in medical imaging. This system employs the novel Selective Local Image Fitting (SLIF) model, a mathematical approach that addresses common challenges in cancer imaging, such as intensity inhomogeneity and low contrast. Unlike traditional methods, MedMathematica segments cancer abnormalities in both color and grayscale images without converting them to grayscale, preserving critical data and enhancing accuracy. Developed to meet the demands of radiologists and healthcare professionals, MedMathematica achieves a high Dice accuracy of 93.32% and a rapid processing time of 0.9483 seconds. Its user-friendly interface ensures accessibility, even for resource-limited healthcare settings. MedMathematica's impact extends beyond healthcare. By enabling early and precise cancer detection, it improves patient outcomes and reduces healthcare costs associated with late-stage treatment. Its commercialization potential includes partnerships with healthcare institutions, medical device companies, and academic research centers, offering scalability and adaptability across global markets. This innovation not only exemplifies the power of mathematical modeling in medicine but also contributes to socioeconomic goals by fostering healthcare innovation, improving public health, and supporting national initiatives like Malaysia’s Shared Prosperity Vision 2030