Microwave-based Breast Cancer Detection by using Auto Encoder-Decoder Probabilistic Neural Network

In this paper, our primary objective is to present an AI-based model designed for the detection of breast cancer through the analysis of microwave data. The data utilized in this study were collected by MammoWave scanner, with the magnitude of the \mathbf{S 2 1} complex number serving as the raw dat...

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Main Authors: Taghipour-Gorjikolaie, Mehran, Ghavami, Navid, Tiberi, Gianluigi, Badia, Mario, Papini, Lorenzo, Fracassini, Arianna, Bigotti, Alessandra, Palomba, Gianmarco, Ghavami, Mohammad
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Artificial Intelligence
Auto Encoder-Decoder
Biological neural networks
Breast cancer
Encoding
Feature extraction
Microwave devices
Microwave theory and techniques
Performance evaluation
Probabilistic logic
Probabilistic Neural Network
Sensitivity and specificity
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Summary:In this paper, our primary objective is to present an AI-based model designed for the detection of breast cancer through the analysis of microwave data. The data utilized in this study were collected by MammoWave scanner, with the magnitude of the \mathbf{S 2 1} complex number serving as the raw data input. We employed an Auto Encoder-Decoder for feature extraction, followed by the utilization of a Probabilistic Neural Network for the detection of malignant lesions. The results obtained indicate that although there is an imbalance in the distribution between healthy and non-healthy classes, our proposed method demonstrates the ability to address this challenge effectively through fine-tuning. Our approach achieves an approximate 60 \% rate for accuracy, sensitivity, and specificity, showcasing its promising potential in breast cancer detection.
ISSN:2326-8301
DOI:10.1109/ISMICT61996.2024.10738023