Breast cancer diagnosis based on hybrid SqueezeNet and improved chef-based optimizer

The most frequent disease in women and the one that accounts for the majority of cancer-related fatalities in females is breast cancer. An important milestone in breast cancer CAD systems is the automatic recognition and delineation of masses in mammograms. We have developed a novel technique in thi...

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Veröffentlicht in:Expert systems with applications Jg. 237; S. 121470
Hauptverfasser: Huang, Qirui, Ding, Huan, Effatparvar, Mehdi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2024
Schlagworte:
Breast Cancer
Diagnosis
Improved Version of Chef-based Optimization Algorithm
Mammogram
SqueezeNet
SqueezeNet
Improved Version of Chef-based Optimization Algorithm
Mammogram
Diagnosis
Breast Cancer
ISSN:0957-4174, 1873-6793
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Zusammenfassung:The most frequent disease in women and the one that accounts for the majority of cancer-related fatalities in females is breast cancer. An important milestone in breast cancer CAD systems is the automatic recognition and delineation of masses in mammograms. We have developed a novel technique in this study to identify potential mass candidates in mammograms automatically. The suggested method is a pipeline method with three stages. First, a noise reduction method based on median filtering is performed to eliminate the noises. Then, the region of interest is threshold out of the pre-processed images using the Kapur technique. Afterward, to eliminate useless features, optimum feature extraction and selection are designed. It is established by an Improved version of the Chef-Based Optimization (ICBO) algorithm. Lastly, a classification is implemented based on an optimal SqueezeNet model for the final diagnosis. This classifier is also optimized based on the ICBO algorithm to maximize efficiency. Simulations of the method are applied to the MIAS database, and its results are compared with different state-of-the-art methods to show the method's advantage.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2023.121470