Breast tumor classification based on deep convolutional neural networks

Bakkouri, Ibtissam; Afdel, Karim

doi:10.1109/atsip.2017.8075562

Cited by 26 publications

(8 citation statements)

References 12 publications

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“…Bakkouri and Afdel [ 123 ] proposed a discriminant target for supervised feature learning to classify tumors on mammograms as malignant or benign by training CNNs. The selected data set input image was set to a fixed length, and the region of interest with a normalized size was obtained based on the Gaussian pyramid.…”

Section: Practical Applications Of Convolutional Neural Network In Tumor Diagnosismentioning

confidence: 99%

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

Yan

Yao

Wang

et al. 2021

Biology

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Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.

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Section: Practical Applications Of Convolutional Neural Network In Tumor Diagnosismentioning

confidence: 99%

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

Yan

Yao

Wang

et al. 2021

Biology

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“…Similarly, Jiao, et al [80] also proposed a deep feature based framework combining intensity information for breast masses classification task. In related work, Bakkouri and Afdel [81] proposed a novel discriminative objective for supervised feature deep learning approach focused on the classification of tumors in mammography as malignant or benign, using Softmax layer as a classifier. The proposed network was enhanced with a scaling process based on Gaussian pyramids for obtaining regions of interest with normalized size.…”

Section: ) Architectural Distortionmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Oyelade

Ezugwu

2020

IEEE Access

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Breast cancer is a type of cancer that has risen to be the second cause of death among women. Classification of breast tissues into normal, benign, or malignant depends on the presence of abnormalities like microcalcifications, masses, architectural distortions, and asymmetries. Architectural distortion (AD) is subtle in detection with no association with masses but shows the abnormal arrangement of tissue strands, often in a radial, spiculation, or random pattern. It is widely rated as the third symptom of breast cancer which is the most commonly missed abnormality. Most computational approaches characterizing abnormalities in breast images often concentrate on the detection of microcalcification and masses with architectural distortions appearing as a secondary finding. The subtle nature and a minimal occurrence of architectural distortions may seem to complicate computational approaches for its detection. As a result, little research interest has been recorded in this area. It is widely reported that some cases of recent breast cancer are wrongly diagnosed due to the omission in detecting the presence of architectural distortion at the early stage of the disease. However, we discovered that most computational solutions to early detection of breast cancer are focused mainly on detecting other abnormalities such as masses and microcalcification, which are some evidence of the advanced stage of the disease. To emphasise the little efforts channeled towards detection of AD compared to other abnormalities, this article aims to detail the review of such studies in the last decade. To the best of our knowledge, this study presents the first review which focuses on the detection of architectural distortion (AD) from mammographic images. Furthermore, this article presents a comprehensive review of approaches, advances, and challenges on the computational methods for detecting AD, with the sole aim of advancing the use of deep learning models in detecting AD. Moreover, a comparative study of performance analyses of articles surveyed in this article is investigated. Our findings revealed that about 70% of the existing literature adopted Gabor Filters, while just less than 10% leveraged on the state-ofthe-art performances recorded in computer vision and deep learning, in building outstanding computational models for the detection of AD. The current study also discovered that using a deep learning approach, such as the convolution neural network (CNN) method, can yield a significant increase in performance for the task of detection of architectural distortions. This assertion is based on literature results obtained using the CNN, which generates an accuracy of 99.4% compared to the use of Gabor filters method, which accounts for 95% accuracy.

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“…Although the above research achieved some satisfactory results, the datasets were either too small or from different ultrasonic machines, making it difficult to implement generalization. In a few attempts to locate and classify tumors [7,8], the overall performance of automatically locating regions of interest and classifying breast lesions employing different Convolutional Neural Network (CNN) architecture has been improved, comparing with traditional classification algorithms [9].…”

Section: Introductionmentioning

confidence: 99%

Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images

Tian

et al. 2021

BioMed Eng OnLine

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Background The rapid development of artificial intelligence technology has improved the capability of automatic breast cancer diagnosis, compared to traditional machine learning methods. Convolutional Neural Network (CNN) can automatically select high efficiency features, which helps to improve the level of computer-aided diagnosis (CAD). It can improve the performance of distinguishing benign and malignant breast ultrasound (BUS) tumor images, making rapid breast tumor screening possible. Results The classification model was evaluated with a different dataset of 100 BUS tumor images (50 benign cases and 50 malignant cases), which was not used in network training. Evaluation indicators include accuracy, sensitivity, specificity, and area under curve (AUC) value. The results in the Fus2Net model had an accuracy of 92%, the sensitivity reached 95.65%, the specificity reached 88.89%, and the AUC value reached 0.97 for classifying BUS tumor images. Conclusions The experiment compared the existing CNN-categorized architecture, and the Fus2Net architecture we customed has more advantages in a comprehensive performance. The obtained results demonstrated that the Fus2Net classification method we proposed can better assist radiologists in the diagnosis of benign and malignant BUS tumor images. Methods The existing public datasets are small and the amount of data suffer from the balance issue. In this paper, we provide a relatively larger dataset with a total of 1052 ultrasound images, including 696 benign images and 356 malignant images, which were collected from a local hospital. We proposed a novel CNN named Fus2Net for the benign and malignant classification of BUS tumor images and it contains two self-designed feature extraction modules. To evaluate how the classifier generalizes on the experimental dataset, we employed the training set (646 benign cases and 306 malignant cases) for tenfold cross-validation. Meanwhile, to solve the balance of the dataset, the training data were augmented before being fed into the Fus2Net. In the experiment, we used hyperparameter fine-tuning and regularization technology to make the Fus2Net convergence.

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Breast tumor classification based on deep convolutional neural networks

Cited by 26 publications

References 12 publications

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

A Survey of Computer-Aided Tumor Diagnosis Based on Convolutional Neural Network

A State-of-the-Art Survey on Deep Learning Methods for Detection of Architectural Distortion From Digital Mammography

Fus2Net: a novel Convolutional Neural Network for classification of benign and malignant breast tumor in ultrasound images

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