Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images

Abstract

Inspired by Connected-UNets, this study proposes a deep learning model, called Connected-SegNets, for breast tumor segmentation from X-ray images. In the proposed model, two SegNet architectures are connected with skip connections between their layers. Moreover, the cross-entropy loss function of the original SegNet has been replaced by the intersection over union (IoU) loss function in order to make the proposed model more robust against noise during the training process. As part of data preprocessing, a histogram equalization technique, called contrast limit adapt histogram equalization (CLAHE), is applied to all datasets to enhance the compressed regions and smooth the distribution of the pixels. Additionally, two image augmentation methods, namely rotation and flipping, are used to increase the amount of training data and to prevent overfitting. The proposed model has been evaluated on two publicly available datasets, specifically INbreast and the curated breast imaging subset of digital database for screening mammography (CBIS-DDSM). The proposed model has also been evaluated using a private dataset obtained from Cheng Hsin General Hospital in Taiwan. The experimental results show that the proposed Connected-SegNets model outperforms the state-of-the-art methods in terms of Dice score and IoU score. The proposed Connected-SegNets produces a maximum Dice score of 96.34% on the INbreast dataset, 92.86% on the CBIS-DDSM dataset, and 92.25% on the private dataset. Furthermore, the experimental results show that the proposed model achieves the highest IoU score of 91.21%, 87.34%, and 83.71% on INbreast, CBIS-DDSM, and the private dataset, respectively.

A Systematic Literature Review on Mammography: Deep Learning in Redefining Breast Cancer Diagnosis for the Asian Perspective

“…Adaptive Neuro Fuzzy Classifier (Rani et al, 2023) Due to its structure, which incorporates the intuitive nature of fuzzy logic systems with the learning capabilities of neural networks, this approach is employed for handling complex relationships in data. (Alkhaleefah et al, 2022;Abdelhafiz et al, 2020;Ashwini et al, 2023;Bai et al, 2022;Boudouh and Bouakkaz, 2023a;Lingampally and Kavuri, 2023;Liu et al, 2023;Mahmood et al, 2021Mahmood et al, , 2022Malathi and Latha, 2023;Maqsood et al, 2022;Cao et al, 2020;Castro-Tapia et al, 2021;Ertugrul and Abdullah, 2022;El Houby and Yassin, 2021;Falconi et al, 2020;George Melekoodappattu et al, 2022;Gerbasi et al, 2023;Gnanasekaran et al, 2020;Harris et al, 2023;Montaha et al, 2021;Huang and Lin, 2021;Jayandhi et al, 2022;Karthiga et al, 2022 (Cai et al, 2021;Narayanan et al, 2022;Nithya and Santhi, 2021;Oyelade and Ezugwu, 2022;Oza et al, 2022;Pawar et al, 2022;Ragab et al, 2021Ragab et al, , 2019Ravikumar et al, 2023;Kavitha et al, 2021) Histogram Equalization (Ahmad et al, 2023;Alfifi et al, 2020;Arora et al, 2020;Babu and Jerome, 2022;Baccouche et al, 2022;…”

“…Seg-Net (Abdelhafiz et al, 2020) SegNet's encoder-decoder design uses max-pooling indices to retain spatial details while remaining memory efficient unlike U-net. Connected Seg-Net (Alkhaleefah et al, 2022) It is comprised of two encoder and decoder networks. After cascading a second SegNet, additional skip connections are used to connect the first decoder and second encoder networks.…”

The application of traditional machine learning and deep learning techniques in mammography: a review

“…Alkhaleefah et al. ( 70 ) developed a DL model called Connected SegNets for segmenting breast tumors from X-ray images. In the proposed model, two SegNet architectures are connected by skip-the-loop connections between their layers.…”

Correction: Alkhaleefah et al. Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images. Cancers 2022, 14, 4030

“…In the original publication [ 1 ], there was a mistake in Figure 5 as published. Figure 6 was repeated twice.…”