Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network
| العنوان: | Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network |
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| المؤلفون: | Jordina Torrents-Barrena, Md. Mostafa Kamal Sarker, Meritexell Arenas, Vivek Kumar Singh, Hatem A. Rashwan, Farhan Akram, Domenec Puig, Santiago Romani, Miguel Arquez, Adel Saleh, Nidhi Pandey |
| المصدر: | Expert Systems with Applications. 139:112855 |
| بيانات النشر: | Elsevier BV, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, 0209 industrial biotechnology, Ground truth, Intersection (set theory), business.industry, Computer science, Computer Vision and Pattern Recognition (cs.CV), Frame (networking), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, General Engineering, Pattern recognition, 02 engineering and technology, Convolutional neural network, Computer Science Applications, Image (mathematics), ComputingMethodologies_PATTERNRECOGNITION, 020901 industrial engineering & automation, Artificial Intelligence, Region of interest, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Segmentation, Artificial intelligence, business |
| الوصف: | Mammogram inspection in search of breast tumors is a tough assignment that radiologists must carry out frequently. Therefore, image analysis methods are needed for the detection and delineation of breast masses, which portray crucial morphological information that will support reliable diagnosis. In this paper, we proposed a conditional Generative Adversarial Network (cGAN) devised to segment a breast mass within a region of interest (ROI) in a mammogram. The generative network learns to recognize the breast mass area and to create the binary mask that outlines the breast mass. In turn, the adversarial network learns to distinguish between real (ground truth) and synthetic segmentations, thus enforcing the generative network to create binary masks as realistic as possible. The cGAN works well even when the number of training samples are limited. Therefore, the proposed method outperforms several state-of-the-art approaches. This hypothesis is corroborated by diverse experiments performed on two datasets, the public INbreast and a private in-house dataset. The proposed segmentation model provides a high Dice coefficient and Intersection over Union (IoU) of 94% and 87%, respectively. In addition, a shape descriptor based on a Convolutional Neural Network (CNN) is proposed to classify the generated masks into four mass shapes: irregular, lobular, oval and round. The proposed shape descriptor was trained on Digital Database for Screening Mammography (DDSM) yielding an overall accuracy of 80%, which outperforms the current state-of-the-art. Comment: 33 pages, Submitted to Expert Systems with Applications |
| تدمد: | 0957-4174 |
| DOI: | 10.1016/j.eswa.2019.112855 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::866dcc648c8a48cbeac48533aabb8cf4 https://doi.org/10.1016/j.eswa.2019.112855 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....866dcc648c8a48cbeac48533aabb8cf4 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 09574174 |
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| DOI: | 10.1016/j.eswa.2019.112855 |