Just Accepted
Explainable Deep Learning for Breast Cancer Classification and Localisation
Authors: 
Marcello Di Giammarco, Camilla Vitulli, Simone Cirnelli, Benedetta Masone, Antonella santone, Mario Cesarelli, Fabio Martinelli, Francesco MercaldoAuthors Info & Claims
Marcello Di Giammarco, Camilla Vitulli, Simone Cirnelli, Benedetta Masone, Antonella santone, Mario Cesarelli, Fabio Martinelli, Francesco MercaldoAuthors Info & ClaimsAccepted on 15 October 2024
Abstract
Breast cancer is a kind of cancer that forms in the cells of the breasts. After skin cancer, breast cancer represents the most common cancer diagnosed in women in the United States. As a matter of fact, in January 2022, there are more than 3.8 million women with a history of breast cancer in the United States, this is the reason why there is a need for novel methods for automatic breast cancer screening, with the aim of starting any therapy as quickly as possible to try to limit the proliferation of the disease. In this paper, we propose a method aimed at detecting breast cancer through a deep learning network developed by authors. Moreover, the proposed method is able to provide prediction explainability by means of class activation mapping, aimed to automatically highlight the suspicious area on the image. We take into account a way to understand whether the cancer prediction and localisation can be considered robust by analyzing the output of two different class activation mapping algorithms. We evaluate the effectiveness of the proposed method by using a dataset composed of 9016 images obtaining an accuracy equal to 93.5%, thus showing the effectiveness of the proposed network for breast cancer detection and localisation.
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Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Online AM: 01 November 2024
Accepted: 15 October 2024
Revised: 02 October 2024
Received: 21 December 2023
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