Fully convolutional network for automated detection and diagnosis of mammographic masses

Abstract

Breast cancer, though rare in male, is very frequent in female and has high mortality rate which can be reduced if detected and diagnosed at the early stage. Thus, in this paper, deep learning architecture based on U-Net is proposed for the detection of breast masses and its characterization as benign or malignant. The evaluation of the proposed architecture in detection is carried out on two benchmark datasets— INbreast and DDSM and achieved a true positive rate of 99.64% at 0.25 false positives per image for INbreast dataset while the same for DDSM are 97.36% and 0.38 FPs/I, respectively. For mass characterization, an accuracy of 97.39% with an AUC of 0.97 is obtained for INbreast while the same for DDSM are 96.81%, and 0.96, respectively. The measured results are further compared with the state-of-the-art techniques where the introduced scheme takes an edge over others.

Computer-Aided Detection and Diagnosis of Breast Cancer: a Review

“…In the literature, this dataset has been used for texture and deep learning-based feature extraction in the experimental setup (Sharma and Purwar, 2020;Sharma and Purwar, 2022) good classification performance on it. Recently, a series of studies used a mini-MIAS dataset for performance evaluation on various deep learning models (Patel and Hadia, 2023;Ranjbarzadeh et al, 2022;Kumar et al, 2022;Rmili et al, 2022;Kulkarni and Rabidas, 2023). Furthermore, Sharma and Purwar (2023) augmented the ROI images extracted from this dataset and achieved an average enhancement of more than 8% of classification accuracy when evaluated over 24 classifiers.…”