A Convolutional Neural Network Approach for The Diagnosis of Breast Cancer

Wadhwa, Gitanjali; Mathur, Mansi

doi:10.1109/pdgc50313.2020.9315817

Cited by 8 publications

(3 citation statements)

References 18 publications

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“…Therefore, given a large enough set of input data, the network can learn a set of characteristics that allow it to classify new data. They are suitable for clinical decision-making due to their ability to learn complex data patterns not visible in the medical chart [ 32 , 33 ].…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…Several research have examined ANN [8][9] [10]. A neural network is a highly parallelized and distributed computing system consisting of a vast number of interconnected neurons, which serve as individual units for Processing of info.…”

Section: Related Workmentioning

confidence: 99%

Fault Identification in Optical Transport Network Using CNN

Ravindra L. Pardhi

2024

jes

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The growing diversity Regarding the provision of information and services transmitted Using optical technology transport networks (OTN) has made network survival a crucial issue in current study. Fault detection refers to the process of identifying faults that arise from various issues such as packet loss, disconnection, and others in the OTN. The determination of the fault location in an OTN is highly significant in the analysis of the resilience of optical networks. This study presents a fault diagnosis system that utilises a Convolutional Neural Network (CNN), focusing primarily on distinguishing between hard faults (HF) and soft faults (SF). The CNN is implemented in where the fault is situated domain of OTN to determine the presence or absence of potential fault locations. The notion An F-Measure is implemented in order to quantify the impact of positioning.utilising Location, time, mean squared error (MSE), and F-measure. The scientific investigation demonstrates that the suggested CNN neural network achieves the highest performance. The suggested CNN has a reduced localization time and achieves an F1-score of 0.98 after 85 iterations. This level of accuracy and real-time performance fulfils the requirements for fault identification. Hence, there is significant potential and practical utility in incorporating neural networks in the field of identifying and locating faults in optical transport networks.

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“…The latest studies of breast cancer detection and classification have achieved different performance and accuracy with different image preprocessing techniques [ 14 , 15 ], CNN architectures [ 16 ], activation functions [ 17 ], and optimization algorithms [ 18 , 19 ], and whether it applied as patches or images [ 20 , 21 ]. Many research studies show that the CNN overcomes the limitation of classical machine learning methods and achieved better results in the detection and classification accuracies of breast cancer [ 22 , 23 ]. Moreover, the depth and width of the deep network can help to improve the network's quality [ 24 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

AlEisa

Touiti

Alhussan

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, a new classification approach of breast cancer based on Fully Convolutional Networks (FCNs) and Beta Wavelet Autoencoder (BWAE) is presented. FCN, as a powerful image segmentation model, is used to extract the relevant information from mammography images. It will identify the relevant zones to model while WAE is used to model the extracted information for these zones. In fact, WAE has proven its superiority to the majority of the features extraction approaches. The fusion of these two techniques have improved the feature extraction phase and this by keeping and modeling only the relevant and useful features for the identification and description of breast masses. The experimental results showed the effectiveness of our proposed method which has given very encouraging results in comparison with the states of the art approaches on the same mammographic image base. A precision rate of 94% for benign and 93% for malignant was achieved with a recall rate of 92% for benign and 95% for malignant. For the normal case, we were able to reach a rate of 100%.

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A Convolutional Neural Network Approach for The Diagnosis of Breast Cancer

Cited by 8 publications

References 18 publications

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

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

Fault Identification in Optical Transport Network Using CNN

Breast Cancer Classification Using FCN and Beta Wavelet Autoencoder

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