Identification and selection of the best artificial intelligence methods developed for detection and diagnosis of breast cancer

Abstract

Background:

Comprehensive identification and prioritization of developed artificial intelligence methods for the detection and diagnosis of breast cancer can help to select proper techniques. This study aimed to introduce the best artificial intelligence techniques developed for the detection and diagnosis of breast cancer using microscopic images by fuzzy AHP-TOPSIS techniques.

Methods:

To identify the artificial intelligence techniques developed, a systematic search was performed in five reliable databases. After that, the Delphi method was applied to determine the proper criteria for selecting the best artificial intelligence techniques. To estimate the relative weights of the criteria, the fuzzy analytical hierarchy process (FAHP) method was used. In the next step, to prioritize the identified artificial intelligence techniques, the technique for order of preference by similarity to the ideal solution (TOPSIS) method was applied.

Results:

Forty-four artificial intelligence techniques were identified. Seven selection criteria, validity, accuracy, comprehensiveness, processing time, cost, simplicity, and executive capability, were introduced. Ensemble deep learning architectures integrated with web of things (weight = 0.8041), the computer-aided diagnosis method (weight = 0.7774), the ensemble strategy (VGG16 - ResNet34 - ResNet50) (weight = 0.7475), automated tumor-stroma interface zone detection (weight = 0.7475), and MultiNet/Computer-Aided Diagnosis (CAD)-based deep learning model (weight = 0.7262) were selected as the best methods, respectively.

Conclusion:

These findings represent a total approach to the developed techniques which can be used for designing methods with better performance in the future.