Breast cancer detection from MR images through an auto-probing discrete Fourier transform system

Sim, K. S.; Chia, F.K.; Nia, M. E.; Tso, C.P.; Chong, Ai-Xin; Abbas, Siti Fathimah; Chong, Siew-Chin

doi:10.1016/j.compbiomed.2014.03.003

Cited by 11 publications

(1 citation statement)

References 33 publications

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“…The discrepancy for differentiated diagnosis opinion among medical experts and radiologists require dual reading procedure (Yap & Yap, 2016). Another approach is semi-automatically detect and classify the breast cancer abnormalities (Sim et al, 2014), . It is challenging to apply common image processing techniques to locate various types of mammograms from the medical images since malignant lesions can appear at different locations and have different intensity distributions.…”

Section: Introductionmentioning

confidence: 99%

Deep residual learning with attention mechanism for breast cancer classification

Toa,

Elsayed,

Sim

2023

Soft Comput

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Invasive Ductal Carcinoma (IDC) is a common form of breast cancer that can be found in women. In traditional medical practice, physicians have to manually test and classify the areas which they suspect to be cancerous. However, the literature strongly shows that that the process manual segmentation done by the medical practitioners, is neither time-efficient nor accurate as it depends on their subjective judgment. The model called Residual Attention Neural Network Breast Cancer Classification (RANN-BCC) is introduced in this paper to help medical practitioners in the cancer diagnostic process. RANN-BCC utilizes Residual Neural Network (ResNet) as an expert-supportive method to help medical practitioners in cancer diagnosis. The implementation of RANN-BCC can support the classification of Whole Slide Imaging (WSI) into non-IDC and IDC without prior information about the presence of a cancerous lesion. The result of classification shows that the RANN-BCC model achieved 92.45% accuracy, 0.98 recall, 0.91 precision, and 0.94 F-score which outperform other models such as CNN, AlexNet, Residual Neural Network 34 (ResNet34), and Feed Forward Neural Network. The developed RANN-BCC model aims to help medical experts classify IDC and non-IDC of breast cancer by learning the feature content of medical images.

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Section: Introductionmentioning

confidence: 99%

Deep residual learning with attention mechanism for breast cancer classification

Toa,

Elsayed,

Sim

2023

Soft Comput

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Breast DCE-MRI Segmentation for Lesion Detection Using Clustering with Multi-verse Optimization Algorithm

Kar¹,

2021

Advances in Intelligent Systems and Computing

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Breast DCE-MRI Segmentation for Lesion Detection Using Clustering with Fireworks Algorithm

Mukhopadhyay

2021

Algorithms for Intelligent Systems

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Breast cancer detection from MR images through an auto-probing discrete Fourier transform system

Cited by 11 publications

References 33 publications

Deep residual learning with attention mechanism for breast cancer classification

Deep residual learning with attention mechanism for breast cancer classification

Breast DCE-MRI Segmentation for Lesion Detection Using Clustering with Multi-verse Optimization Algorithm

Breast DCE-MRI Segmentation for Lesion Detection Using Clustering with Fireworks Algorithm

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