Publication

Abstract : Cancer is a major killer of humans and one of the most preventable diseases. Early detection of cancer cells is an important aspect to implement diagnosis techniques and related aid. Image processing, classification, and other approaches are potential tools for the detection and diagnosis of cancer cells. With the advent of emerging diagnosis techniques, the disease still has a high mortality rate. Image processing, artificial neural network methods, area measuring, and cell clump identification are some important techniques used to locate cancer cells in the suggested approach. The proposed work allows for the automatic detection of malignant features in any CT scan, mammogram, or biopsy specimen. The work implements several pre-processing steps on the input images, it includes grey scaling, binarization, inversion, and a flood fill operation. The proposed method also aims for the automatic detection of cancer cells in a novel way with a fine-tuned feedback mechanism. The work anticipates a better detection of cancer cells which will contribute to medical science research.