Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images

George, Yasmeen; Zayed, Hala H.; Roushdy, Mohamed; Elbagoury, Bassant M.

doi:10.1109/jsyst.2013.2279415

Cited by 180 publications

(79 citation statements)

References 27 publications

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“…Mainly, DCT is used for those processes in which low‐frequency content (such as nature frequencies of the body), should be considered. Nevertheless, the discrete fourier transform (DFT) offers a better means or intentions for spectral analysis, and the maps draw those results to very simple physical frequencies . The great advantage of DCT calculation is that it contains the required frequencies based on the size of the image, and the calculations will be meaningful and, accordingly, the DCT is chosen for this study.…”

Section: Methodsmentioning

confidence: 99%

Early diagnosis of skin cancer by ultrasound frequency analysis

Kia

Setayeshi

Pouladian

et al. 2019

J Applied Clin Med Phys

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The diagnosis of cancer by modern computer tools, at the very first stages of the incident, is a very important issue that has involved many researchers. In the meantime, skin cancer is a great deal of research because many people are involved with it. The purpose of this paper is to introduce an innovative method based on tissue frequency analyzes to obtain the accurate and real‐time evaluation of skin cancers. According to the Biological resonance theory, body cells have natural and unique frequencies based on their biological fluctuations, which, if the structure, profile and cellular status change, its frequency also varies. This concept and theory is considered as the basis for analyzing skin tissue health in the proposed method. Reflected ultrasound waves from tissue have been processed and studied based on frequency analysis as a new method for early detection and diagnosis of accurate location and type of skin diseases. The developed algorithm was approved through 400 patients from CRED; its ability to evaluate benign and malignant skin lesions was shown (AUC = 0.959), with comparable clinical precision; as for the selected threshold, sensitivity, and specificity were 93.8% and 97.3%, respectively. Therefore, this method can detect skin malignancy with an accurate, noninvasive and real‐time procedure.

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Section: Methodsmentioning

confidence: 99%

Early diagnosis of skin cancer by ultrasound frequency analysis

Kia

Setayeshi

Pouladian

et al. 2019

J Applied Clin Med Phys

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“…The best reported effectiveness is up to 98.51%. In 2014, George et al (2014) proposed a diagnosis system for breast cancer using nuclear segmentation based on cytological images. Four classification models were used, including MLP (multilayer perceptron using the backpropagation algorithm), PNN (probabilistic neural network), LVQ (learning vector quantization), and SVM.…”

Section: Related Workmentioning

confidence: 99%

“…Four classification models were used, including MLP (multilayer perceptron using the backpropagation algorithm), PNN (probabilistic neural network), LVQ (learning vector quantization), and SVM. The parameters for each model can be found in Table 5 in George et al (2014). The classification accuracy using 10-fold cross-validation is 76~94% with only 92 images, including 45 images of benign tumors and 47 images of malignant tumors.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Analysis of Histopathological Images of Breast Cancer

et al. 2019

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Breast cancer is associated with the highest morbidity rates for cancer diagnoses in the world and has become a major public health issue. Early diagnosis can increase the chance of successful treatment and survival. However, it is a very challenging and time-consuming task that relies on the experience of pathologists. The automatic diagnosis of breast cancer by analyzing histopathological images plays a significant role for patients and their prognosis. However, traditional feature extraction methods can only extract some low-level features of images, and prior knowledge is necessary to select useful features, which can be greatly affected by humans. Deep learning techniques can extract high-level abstract features from images automatically. Therefore, we introduce it to analyze histopathological images of breast cancer via supervised and unsupervised deep convolutional neural networks. First, we adapted Inception_V3 and Inception_ResNet_V2 architectures to the binary and multi-class issues of breast cancer histopathological image classification by utilizing transfer learning techniques. Then, to overcome the influence from the imbalanced histopathological images in subclasses, we balanced the subclasses with Ductal Carcinoma as the baseline by turning images up and down, right and left, and rotating them counterclockwise by 90 and 180 degrees. Our experimental results of the supervised histopathological image classification of breast cancer and the comparison to the results from other studies demonstrate that Inception_V3 and Inception_ResNet_V2 based histopathological image classification of breast cancer is superior to the existing methods. Furthermore, these findings show that Inception_ResNet_V2 network is the best deep learning architecture so far for diagnosing breast cancers by analyzing histopathological images. Therefore, we used Inception_ResNet_V2 to extract features from breast cancer histopathological images to perform unsupervised analysis of the images. We also constructed a new autoencoder network to transform the features extracted by Inception_ResNet_V2 to a low dimensional space to do clustering analysis of the images. The experimental results demonstrate that using our proposed autoencoder network results in better clustering results than those based on features extracted only by Inception_ResNet_V2 network. All of our experimental results demonstrate that Inception_ResNet_V2 network based deep transfer learning provides a new means of performing analysis of histopathological images of breast cancer.

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“…In the past few years, several works aimed at breast cancer detection and classification using CNNs have been published [7,8,9,10,11,5,12]. Although the aim of all of these works are very similar, each work considers a specific type of problem.…”

Section: Related Workmentioning

confidence: 99%

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

Nazeri

Aminpour

Ebrahimi

2018

Lecture Notes in Computer Science

103

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This paper explores the problem of breast tissue classification of microscopy images. Based on the predominant cancer type the goal is to classify images into four categories of normal, benign, in situ carcinoma, and invasive carcinoma. Given a suitable training dataset, we utilize deep learning techniques to address the classification problem. Due to the large size of each image in the training dataset, we propose a patch-based technique which consists of two consecutive convolutional neural networks. The first "patch-wise" network acts as an auto-encoder that extracts the most salient features of image patches while the second "image-wise" network performs classification of the whole image. The first network is pre-trained and aimed at extracting local information while the second network obtains global information of an input image. We trained the networks using the ICIAR 2018 grand challenge on BreAst Cancer Histology (BACH) dataset. The proposed method yields 95% accuracy on the validation set compared to previously reported 77% accuracy rates in the literature. Our code is publicly available at https://github.com/ImagingLab/ICIAR2018.

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Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images

Cited by 180 publications

References 27 publications

Early diagnosis of skin cancer by ultrasound frequency analysis

Early diagnosis of skin cancer by ultrasound frequency analysis

Deep Learning Based Analysis of Histopathological Images of Breast Cancer

Two-Stage Convolutional Neural Network for Breast Cancer Histology Image Classification

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