Segmentation of tumour from mammogram images using U-SegNet: a hybrid approach

Ravikumar, M.; Rachana, P. G.; Shivaprasad, B. J.

doi:10.1080/21681163.2022.2072769

Cited by 1 publication

(1 citation statement)

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“…It achieved dice scores of 92.86% and 96.34% and IoU (intersection over Union) scores of 91.21% and 87.34% on the CBIS-DDSM and INbreast datasets, respectively. Several authors suggested different state-of-the-art ROI segmentation techniques for breast cancer detection (Vidal et al, 2022;Cho and Yoon, 2021;Ravikumar et al, 2023;Alam et al, 2023).…”

Section: Segmentationmentioning

confidence: 99%

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

Sharma,

Purwar

2024

ADCAIJ

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Statistics across different countries point to breast cancer being among severe cancers with a high mortality rate. Early detection is essential when it comes to reducing the severity and mortality of breast cancer. Researchers proposed many computer-aided diagnosis/detection (CAD) techniques for this purpose. Many perform well (over 90% of classification accuracy, sensitivity, specificity, and f-1 sore), nevertheless, there is still room for improvement. This paper reviews literature related to breast cancer and the challenges faced by the research community. It discusses the common stages of breast cancer detection/ diagnosis using CAD models along with deep learning and transfer learning (TL) methods. In recent studies, deep learning models outperformed the handcrafted feature extraction and classification task and the semantic segmentation of ROI images achieved good results. An accuracy of up to 99.8% has been obtained using these techniques. Furthermore, using TL, researchers combine the power of both, pre-trained deep learning-based networks and traditional feature extraction approaches.

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Section: Segmentationmentioning

confidence: 99%