Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Jos�Escorcia-Gutierrez,; Mansour, Romany F.; Bele駉, Kelvin; Jim閚ez-Cabas, Javier; P閞ez, Meglys; Madera, Natasha; Velasquez, Kevin

doi:10.32604/cmc.2022.022322

Cited by 24 publications

(10 citation statements)

References 24 publications

(21 reference statements)

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“…Autoencoders combine an encoder and a decoder to reconstruct the input information using a compressed representation. (Escorcia-Gutierrez et al, 2022) It combines wavelet analysis and neural networks. It uses wavelets to extract time-frequency features from input data and then combines these features into a neural network to learn complex patterns.…”

Section: Techniques References Descriptionmentioning

confidence: 99%

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

Amin,

Acharya,

et al. 2024

Preprint

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Breast cancer (BC) is a leading cause of mortality in women worldwide, and its incidence and prognosis vary greatly, especially in low-income areas such as Asia and Africa, compared to America and Europe. This review examines the critical role of computer-aided diagnostic systems utilizing Deep Learning (DL) techniques in improving the precision of BC detection, which can help researchers and practitioners better understand the obstacles and emerging trends in the field. The comprehensive analysis and synthesis of the published articles is provided by the Preferred Reporting Items for Systematic Reviews and Meta- Analyses (PRISMA). Following several searches, 287 articles were found eligible for the assessment followed by data extraction and synthesis. This work provides insight into the pathology of BC and its corresponding mammographic appearance, a comprehensive analysis of publication trends, important contributors, and emerging themes using bibliometric techniques in addition to a thorough review of research articles. Focus maps were also created to identify and shed light on the body of knowledge. In contrast to other research, this review sheds light on the subtle identification of breast density, mass, and calcification from mammography images by emphasizing the critical evaluation of image pre- processing, augmentation, detection, segmentation, and classification techniques used from 2018 to 2023, further highlighting articles related to the Asian dataset. Furthermore, the review examines contributions made by authors, countries, and experimental datasets, providing insights into various approaches. Prospero registration: CRD42022478896

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Section: Techniques References Descriptionmentioning

confidence: 99%

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

Amin,

Acharya,

et al. 2024

Preprint

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“…The findings demonstrated that the suggested model achieves accuracy, specificity, and sensitivity rates of 91.5%, 72.4%, and 94.1%, respectively. In 39 , an automated DL-based BC diagnosis (ADL-BCD) algorithm was introduced utilizing mammograms. The feature extraction step used the pretrained ResNet34, and its parameters were optimized using the chimp optimization algorithm (COA).…”

Section: Related Workmentioning

confidence: 99%

“…According to empirical analysis, when it comes to the training-test ratio, the best results are obtained when 70–90% of the initial data are used for training and the rest are used for testing 43 , 44 . In addition, 70%, 80%, and 90% dataset splitting ratios are most frequently used for training, as seen in 12 , 13 , 18 , 23 , 31 , 39 , and 16 , 30 , 39 , 41 , respectively.…”

Section: Related Workmentioning

confidence: 99%

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

Elkorany

Elsharkawy

2023

Sci Rep

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Breast cancer (BC) is spreading more and more every day. Therefore, a patient's life can be saved by its early discovery. Mammography is frequently used to diagnose BC. The classification of mammography region of interest (ROI) patches (i.e., normal, malignant, or benign) is the most crucial phase in this process since it helps medical professionals to identify BC. In this paper, a hybrid technique that carries out a quick and precise classification that is appropriate for the BC diagnosis system is proposed and tested. Three different Deep Learning (DL) Convolution Neural Network (CNN) models—namely, Inception-V3, ResNet50, and AlexNet—are used in the current study as feature extractors. To extract useful features from each CNN model, our suggested method uses the Term Variance (TV) feature selection algorithm. The TV-selected features from each CNN model are combined and a further selection is performed to obtain the most useful features which are sent later to the multiclass support vector machine (MSVM) classifier. The Mammographic Image Analysis Society (MIAS) image database was used to test the effectiveness of the suggested method for classification. The mammogram's ROI is retrieved, and image patches are assigned to it. Based on the results of testing several TV feature subsets, the 600-feature subset with the highest classification performance was discovered. Higher classification accuracy (CA) is attained when compared to previously published work. The average CA for 70% of training is 97.81%, for 80% of training, it is 98%, and for 90% of training, it reaches its optimal value. Finally, the ablation analysis is performed to emphasize the role of the proposed network’s key parameters.

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“…Table 1 shows the Results of comparing the study with some of the other studies that aimed to improve breast cancer diagnostic images using several techniques. They were compared in terms of samples, methods, results, and the contribution of each study [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

Alshamrani

Alqahtani

et al. 2022

Sensors

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In the world, one in eight women will develop breast cancer. Men can also develop it, but less frequently. This condition starts with uncontrolled cell division brought on by a change in the genes that regulate cell division and growth, which leads to the development of a nodule or tumour. These tumours can be either benign, which poses no health risk, or malignant, also known as cancerous, which puts patients’ lives in jeopardy and has the potential to spread. The most common way to diagnose this problem is via mammograms. This kind of examination enables the detection of abnormalities in breast tissue, such as masses and microcalcifications, which are thought to be indicators of the presence of disease. This study aims to determine how histogram-based image enhancement methods affect the classification of mammograms into five groups: benign calcifications, benign masses, malignant calcifications, malignant masses, and healthy tissue, as determined by a CAD system of automatic mammography classification using convolutional neural networks. Both Contrast-limited Adaptive Histogram Equalization (CAHE) and Histogram Intensity Windowing (HIW) will be used (CLAHE). By improving the contrast between the image’s background, fibrous tissue, dense tissue, and sick tissue, which includes microcalcifications and masses, the mammography histogram is modified using these procedures. In order to help neural networks, learn, the contrast has been increased to make it easier to distinguish between various types of tissue. The proportion of correctly classified images could rise with this technique. Using Deep Convolutional Neural Networks, a model was developed that allows classifying different types of lesions. The model achieved an accuracy of 62%, based on mini-MIAS data. The final goal of the project is the creation of an update algorithm that will be incorporated into the CAD system and will enhance the automatic identification and categorization of microcalcifications and masses. As a result, it would be possible to increase the possibility of early disease identification, which is important because early discovery increases the likelihood of a cure to almost 100%.

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Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Cited by 24 publications

References 24 publications

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

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

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

Enhancement of Mammographic Images Using Histogram-Based Techniques for Their Classification Using CNN

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