MAMMO-Net: An Approach for Classification of Breast Cancer using CNN with Gabor Filter in Mammographic Images

Shajal Afaq, Anamika Jain

20222022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)10 citationsDOI

Abstract

In today’s times, one of the most prevalent form of cancer in women is breast. Early detection of breast cancer improves patients’ chances of survival by allowing them to get the best treatment available. Convolutional Neural Networks has been very popular to extract the relevant features and classification. To develop a real time breast cancer model, we have developed a light weighted convolutional Neural Network (MAAMMO-Net). In this work we have proposed a convolutional Neural Network (MAMMO-Net) for automated diagnosis of breast cancer. We have also processed the mammographic images before giving input to the MAMMO-Net by applying Gabor filter. We experimented with the publicly accessible mammographic dataset CBISDDSM to verify the performance of the suggested model and obtained accuracy of 98%.

Topics & Concepts

Convolutional neural networkComputer scienceGabor filterBreast cancerArtificial intelligencePattern recognition (psychology)Net (polyhedron)Artificial neural networkFilter (signal processing)MammographyCancerComputer visionFeature extractionMedicineMathematicsInternal medicineGeometryAI in cancer detectionInfrared Thermography in MedicineBrain Tumor Detection and Classification