PSOWNNs-CNN: A Computational Radiology for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning Methods

Abstract

Early diagnosis of breast cancer is an important component of breast cancer therapy. A variety of diagnostic platforms can provide valuable information regarding breast cancer patients, including image-based diagnostic techniques. However, breast abnormalities are not always easy to identify. Mammography, ultrasound, and thermography are some of the technologies developed to detect breast cancer. Using image processing and artificial intelligence techniques, the computer enables radiologists to identify chest problems more accurately. The purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods. Compared to current approaches, such as CNN, our particle swarm optimized wavelet neural network (PSOWNN) method appears to be relatively superior. The use of machine learning methods is clearly beneficial in terms of improved performance, efficiency, and quality of images, which are crucial to the most innovative medical applications. According to a comparison of the process’s 905 images to those of other illnesses, 98.6% of the disorders are correctly identified. In summary, PSOWNNs, therefore, have a specificity of 98.8%. Furthermore, PSOWNNs have a precision of 98.6%, which means that, despite the high number of women diagnosed with breast cancer, only 830 (95.2%) are diagnosed. In other words, 95.2% of images are correctly classified. PSOWNNs are more accurate than other machine learning algorithms, SVM, KNN, and CNN.

Breast cancer diagnosis: a survey of pre-processing, segmentation, feature extraction and classification

“…In this proposed method accessible informational index from the mini-mammographic image analysis society (MIAS) database was used. In [64], the purpose of this article was to review various approaches to detecting breast cancer using artificial intelligence (AI) and image processing. The authors present an innovative approach for identifying breast cancer using machine learning methods.…”

Machine learning and new insights for breast cancer diagnosis

“…174 The current review extracted and organized the data in tabular form and summarized the application of CT, breast thermal imaging, PET and microwave imaging technology in the diagnosis of breast cancer (Table 4). 92,109,147,149,151,167,[174][175][176][177][178][179][180][181][182][183][184][185][186][187]…”

“…Models, classes and performance for breast thermographic techniques, positron emission tomography and other combined examination data in selected papers 92,109,147,149,167,[174][175][176][177][178][179][180][181][182][183][184][185][186][187] Paper reference Sakai A, Onishi Y, Matsui M, et alA method for the automated classification of benign and malignant masses on digital breast tomosynthesis images using machine learning and radiomic features. Radiol Phys Technol 2020; 13: 27-36.…”

Addressing the Clinical Feasibility of Adopting Circulating miRNA for Breast Cancer Detection, Monitoring and Management with Artificial Intelligence and Machine Learning Platforms

“…For instance, an ensemble deep learning approach for the definite classification of non-carcinoma and carcinoma BC histopathology images was able show a sensitivity of 97.73% for carcinoma classification, with an overall accuracy of 95.29% [ 91 ]. On the other hand, particle-swarm-optimized wavelet neural network (PSOWNN) was found relatively superior compared to other conventional ML techniques, such as CNN, KNN and SVM [ 92 ]. Meanwhile, the deep-learning-assisted efficient AdaBoost algorithm (DLA-EABA), a combined ML approach with AdaBoost algorithm as the base, for early BC detection showed a high accuracy level of 97.2%, sensitivity at 98.3% and specificity at 96.5% [ 93 ].…”