breast cancer diagnosis and classification improvement based on deep learning and image processing

Nowadays, medical intelligence detection systems have evolved significantly due to advancements in artificial intelligence, however, they face some challenges. breast cancer diagnosis and classification is one of the medical intelligence systems. there are a variety of screening techniques available to detect breast cancer such as mammography, magnetic resonance imaging, and ultrasound. this research uses the mias mammography image dataset and tries to diagnose and classify benign and malignant masses based on image processing and machine learning techniques. initially, we apply pre-processing for noise reduction and image enhancement using quantum inverse mft, and then image segmentation with the social spider algorithm. the type of mass is then diagnosed by the convolutional neural network. the results show that the proposed approach has better performance in comparison to others based on some evaluation criteria such as accuracy of 99.57%, sensitivity of 91%, and specificity of 86%.

breast cancer diagnosis and classification improvement based on deep learning and image processing

nowadays, medical intelligence detection systems have evolved significantly due to advancements in artificial intelligence, however, they face some challenges. breast cancer diagnosis and classification is one of the medical intelligence systems. there are a variety of screening techniques available to detect breast cancer such as mammography, magnetic resonance imaging, and ultrasound. this research uses the mias mammography image dataset and tries to diagnose and classify benign and malignant masses based on image processing and machine learning techniques. initially, we apply pre-processing for noise reduction and image enhancement using quantum inverse mft, and then image segmentation with the social spider algorithm. the type of mass is then diagnosed by the convolutional neural network. the results show that the proposed approach has better performance in comparison to others based on some evaluation criteria such as accuracy of 99.57%, sensitivity of 91%, and specificity of 86%.