Breast Cancer Detection Based on Deep Learning Technique

Ismail, Nur Syahmi; Cheab, Sovuthy

doi:10.1109/encon.2019.8861256

Cited by 55 publications

(13 citation statements)

References 8 publications

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“…So, results need to improve here. In this paper [ 37 ], an author proposed a method for the classification of breast cancer tumors using VGG and ResNet-50 on the IRMA dataset. The models achieved accuracies and sensitivities are 94% for VGG-16, 91.7% for ResNet-50% and 99% for VGG-16, and 94% for ResNet-50%, respectively.…”

Section: Related Workmentioning

confidence: 99%

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Mohiyuddin

Basharat

Ghani

et al. 2022

Computational and Mathematical Methods in Medicine

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Breast cancer incidence has been rising steadily during the past few decades. It is the second leading cause of death in women. If it is diagnosed early, there is a good possibility of recovery. Mammography is proven to be an excellent screening technique for breast tumor diagnosis, but its detection and classification in mammograms remain a significant challenge. Previous studies’ major limitation is an increase in false positive ratio (FPR) and false negative ratio (FNR), as well as a drop in Matthews correlation coefficient (MCC) value. A model that can lower FPR and FNR while increasing MCC value is required. To overcome prior research limitations, a modified network of YOLOv5 is used in this study to detect and classify breast tumors. Our research is conducted using publicly available datasets Curated Breast Imaging Subset of DDSM (CBIS-DDSM). The first step is to perform preprocessing, which includes image enhancing techniques and the removal of pectoral muscles and labels. The dataset is then annotated, augmented, and divided into 60% for training, 30% for validation, and 10% for testing. The experiment is then performed using a batch size of 8, a learning rate of 0.01, a momentum of 0.843, and an epoch value of 300. To evaluate the performance of our proposed model, our proposed model is compared with YOLOv3 and faster RCNN. The results show that our proposed model performs better than YOLOv3 and faster RCNN with 96% mAP, 93.50% MCC value, 96.50% accuracy, 0.04 FPR, and 0.03 FNR value. The results show that our suggested model successfully identifies and classifies breast tumors while also overcoming previous research limitations by lowering the FPR and FNR and boosting the MCC value.

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Section: Related Workmentioning

confidence: 99%

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Mohiyuddin

Basharat

Ghani

et al. 2022

Computational and Mathematical Methods in Medicine

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“…[1] In his research has shown that using data augmentation technique would result in creating more sample sets which avoids overfitting and boosts the performance of the classifier. [2] have come up with research where they have used two different deep learning model such as VGG16 and ResNet50 to classify normal/abnormal cell and the results were quite promising. Thus, different opinions from different researchers have left the hypothesis in perplexed state and needs to be tested universally.…”

Section: Discussionmentioning

confidence: 99%

“…Transfer learning is used in major instances in deep learning because of its ability to train the deep neural network with comparatively little data as suggested by [2]. Many a times these training may take couple of weeks and might consume high graphic processing.…”

Section: Discussionmentioning

confidence: 99%

Breast Cancer Histopathological Image Classification using Deep Convolutional Neural Network

vm¹

2020

Preprint

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Deep learning has come up with the intense class of models which have potential applications in the field of image classification, video recognition, object recognition, natural language Processing and speech recognition. Mainly, Deep convolutional Neural Network is one of the deep learning models that is used for image classification, that extracts the feature from the images and use these extracted features to classify images (2D or 3D images). In this paper, DCNN is used to classify mammogram images obtained from medical imaging process to detect the benign and malignant cells. The outcome of the study is to bring out the idea behind computing techniques incorporated with medical diagnostics, helping medical professional to take advantage of computer aided diagnostics, ultimately improving the time spent by pathologist to inspect the stained tissues in-turn increasing the survival rates.

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“…Their CAD system achieved 99.7% for mass detection and 97% for classification. Several studies show that the pretrained CNN models such as Resnet [19], Alexnet [20], [21] and GoogleNet [30] demonstrate higher results using unaugmented patches and more enhanced results with augmented ones. Different CNN models [22] [23] show different detection and classification accuracies and performance depending on the application, techniques and datasets used.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

Alhussan¹,

Samee²,

Ghoneim³

et al. 2021

IJACSA

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Breast Cancer Detection Based on Deep Learning Technique

Cited by 55 publications

References 8 publications

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network

Breast Cancer Histopathological Image Classification using Deep Convolutional Neural Network

Evaluating Deep and Statistical Machine Learning Models in the Classification of Breast Cancer from Digital Mammograms

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