Classification of Breast Cancer from Digital Mammography Using Deep Learning

López-Cabrera, José Daniel; Rodríguez, Luis A.; Pérez-Díaz, Marlen

doi:10.4114/intartif.vol23iss65pp56-66

Cited by 18 publications

(7 citation statements)

References 24 publications

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“…Agnes et al [25] proposed the classification of mammographic images using the multiscale allconvolutional neural network (MA-CNN) model. MA-CNN performed feature extraction through convolutional neural networks and classified mammography images into three types: normal, benign, and malignant.…”

Section: Literature Reviewmentioning

confidence: 99%

IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

et al. 2021

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Breast cancer is often a fatal disease that has a substantial impact on the female mortality rate. Rapidly spreading breast cancer is due to the abnormal growth of malignant cells in the breast. Early detection of breast cancer can increase treatment opportunities and patient survival rates. Various screening methods with computer-aided detection systems have been developed for the effective diagnosis and treatment of breast cancer. Image data plays an important role in the medical and health industry. Features are extracted from image datasets through deep learning, as deep learning techniques extract features more accurately and rapidly than other existing methods. Deep learning effectively assists existing methods, such as mammogram screening and biopsy, in examining and diagnosing breast cancer. This paper proposes an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages. The proposed model is employed to detect breast cancer and its stages. The experimental results demonstrate 98.86% and 97.81% accuracy for the training and validation phases, respectively. In addition, they demonstrate accuracies of 99.69%, 99.32%, 98.96%, and 99.32% for detecting ductal carcinoma, lobular carcinoma, mucinous carcinoma, and papillary carcinoma. The results of the proposed intelligent prediction of breast cancer stages empowered with the deep learning (IPBCS-DL) model exhibits higher accuracy than existing state-of-the-art methods, indicating its potential to lower the breast cancer mortality rate.INDEX TERMS Internet of medical things, breast cancer prediction, deep learning, convolutional neural network.

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Section: Literature Reviewmentioning

confidence: 99%

IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

et al. 2021

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“…Although, the proposed shape descriptor achieved an 80% accuracy rate on DDSM. López-Cabrera et al [48] developed a new CAD system based on TL and CNN technique. The proposed CAD system classified the mini-MIAS dataset into three categories that are malignant, benign, and normal.…”

Section: Related Workmentioning

confidence: 99%

“…The overall accuracy of the proposed CAD system is 86.05%. To check whether the DL-based models can transfer external data of mammograms with the various distribution of data, López-Cabrera et al [48] study three existing CNN-based models and developed three new models based on the CNN technique. All six models' performance is tested on four datasets such as DDSM, MIAS, private data (UKy), and INBreast using the ROC performance parameter.…”

Section: Related Workmentioning

confidence: 99%

Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram

Malebary

Hashmi

2021

IEEE Access

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In recent years, deep learning techniques are employed in the mammography processing field to reduce radiologists' costs. Existing breast mass classification systems are implemented using deep learning technologies such as a Convolutional Neural Network (CNN). CNN based systems have attained higher performance than the machine learning-based systems in the classification task of mammography images, but a few issues still exist. Some of these issues are; ignorance of semantic features, analysis limitation to the current patch of images, lost patches in less contrast mammography images, and ambiguity in segmentation. These issues lead to increased false information about patches of mammography image, computational cost, decisions based on current patches, and not recovering the variance of patches intensity. In turn, breast mass classification systems based on convolutional neural networks produced unsatisfactory classification accuracy. To resolve these issues and improve the accuracy of classification on low contrast images, we propose a novel Breast Mass Classification system named BMC. It has improved architecture based on a combination of k-mean clustering, Long Short-Term Memory network of Recurrent Neural Network (RNN), CNN, random forest, boosting techniques to classify the breast mass into benign, malignant, and normal. Further, the proposed BMC system is compared with existing classification systems using two publicly available datasets of mammographic images. Proposed BMC system achieves the sensitivity, specificity, F-measure, and accuracy for the DDSM dataset is 0.97%, 0.98%,0.97%, 0.96% and for the MIAS dataset is 0.97%, 0.97%,0.98%, and 0.95% respectively. Further Area Under Curve (AUC) rate of the proposed BMC system lies between 0.94% -0.97% for DDSM and 0.94%-0.98% for the MIAS dataset. The BMC method worked comparably better than other mammography classification schemes that have previously been invented. Moreover, the Confidence interval statistical test is also employed to determine the classification accuracy of the BMC system using different configurations and neural network parameters.INDEX TERMS Deep learning, convolutional neural network, random forest, breast mass classification, recurrent neural network, boosting, machine learning, mammograms, medical image processing.

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“…The author, through an experiment, determined the optimal DCNN technique for precise categorization through comparison made with different approaches that vary under the hyper-parameters and design. Cabrera et al [13] inquiry depended on this network type for categorizing 3 classes, malignant normal and benign cancer. For this reason, the miniMIAS database employed contains lesser images, and the TL algorithm has been implemented in the Inception v3 pre-trained network.…”

Section: Introductionmentioning

confidence: 99%

Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

Hamza¹

2023

Intelligent Automation &Amp; Soft Computing

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Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses' malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the way for highly intellectual and self-reliant computer-aided diagnosis (CAD) systems since the learning capability of Machine Learning (ML) techniques was constantly improving. This paper presents a new Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification (HTDHDAE-BCC) on Digital Mammograms. The presented HTDHDAE-BCC model examines the mammogram images for the identification of BC. In the HTDHDAE-BCC model, the initial stage of image preprocessing is carried out using an average median filter. In addition, the deep convolutional neural network-based Inception v4 model is employed to generate feature vectors. The parameter tuning process uses the binary spider monkey optimization (BSMO) algorithm. The HTDHDAE-BCC model exploits chameleon swarm optimization (CSO) with the DHDAE model for BC classification. The experimental analysis of the HTDHDAE-BCC model is performed using the MIAS database. The experimental outcomes demonstrate the betterments of the HTDHDAE-BCC model over other recent approaches.

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Classification of Breast Cancer from Digital Mammography Using Deep Learning

Cited by 18 publications

References 24 publications

IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning

Automated Breast Mass Classification System Using Deep Learning and Ensemble Learning in Digital Mammogram

Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

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