Improved noise reduction, segmentation, and classification of cancer masses by quantum inverse-matched filter, social spider algorithm, and improved ELM

Document Type : Original Article

Authors

1 Computer Department, Gorgan Branch, Islamic Azad University, Gorgan, Iran.

2 Computer Department, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.

Abstract

Intelligent medical diagnosis systems have been revolutionized by artificial intelligence, leading to new challenges and opportunities. One such system is the classification and diagnosis of breast cancer masses, which can benefit from early detection and increased treatment options. Various screening techniques are available for breast cancer, including mammography, MRI, and ultrasound, each requiring different images and image processing techniques depending on the type of cancer mass diagnosis. This paper focuses on using the MIAS mammography dataset to diagnose and classify benign, malignant, and suspicious masses based on image processing and machine learning techniques. To achieve this goal, a developed approach is proposed, in which preprocessing operations are performed at first to reduce noise and enhance the image based on the proposed Quantum Inverse MFT method, and then image segmentation is applied using the social spider algorithm based on brightness intensity and edge. Subsequently, feature extraction and classification operations are performed using the Extreme Learning Machine method and its developed model, i.e., Moore Penrose Matrix-Extreme Learning Machine, with the aim of diagnosing the type of cancer mass. The results show that the proposed approach has a higher accuracy rate than other methods in terms of some evaluation criteria such as accuracy, sensitivity, specificity and also ROC and AUC.

Keywords


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