Convolutional Neural Networks for Classifying Cervical Cancer Types Using Histological Images.
Cervical cancer is the most common cancer among women worldwide. The diagnosis and classification of cancer are extremely important, as it influences the optimal treatment and length of survival. The objective was to develop and validate a diagnosis system based on convolutional neural networks (CNN...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 2; pp. 441 - 450 |
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| Autores principales: | , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=162679402&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 162679402 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2023 vid: 36 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 162679402 160623603 162679402 162679402 10.1007/s10278-022-00722-8 162679402 ppf: 441 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Convolutional Neural Networks for Classifying Cervical Cancer Types Using Histological Images. aug: au: Li, Yi-xin Chen, Feng Shi, Jiao-jiao Huang, Yu-li Wang, Mei affil: Department of Obstetrics and Gynecology, Xinhua Hospital Chongming Branch, School of Medicine, Shanghai Jiao Tong University, Shanghai, China sug: subj: Neural Networks (Computer) Cervix Neoplasms Classification Image Interpretation, Computer Assisted Cervix Neoplasms Diagnosis Human Histology Specimen Handling Precision ROC Curve Pathologists Probability Carcinoma, Squamous Cell Diagnosis Adenocarcinoma Diagnosis Task Performance and Analysis Algorithms Funding Source ab: Cervical cancer is the most common cancer among women worldwide. The diagnosis and classification of cancer are extremely important, as it influences the optimal treatment and length of survival. The objective was to develop and validate a diagnosis system based on convolutional neural networks (CNN) that identifies cervical malignancies and provides diagnostic interpretability. A total of 8496 labeled histology images were extracted from 229 cervical specimens (cervical squamous cell carcinoma, SCC, n = 37; cervical adenocarcinoma, AC, n = 8; nonmalignant cervical tissues, n = 184). AlexNet, VGG-19, Xception, and ResNet-50 with five-fold cross-validation were constructed to distinguish cervical cancer images from nonmalignant images. The performance of CNNs was quantified in terms of accuracy, precision, recall, and the area under the receiver operating curve (AUC). Six pathologists were recruited to make a comparison with the performance of CNNs. Guided Backpropagation and Gradient-weighted Class Activation Mapping (Grad-CAM) were deployed to highlight the area of high malignant probability. The Xception model had excellent performance in identifying cervical SCC and AC in test sets. For cervical SCC, AUC was 0.98 (internal validation) and 0.974 (external validation). For cervical AC, AUC was 0.966 (internal validation) and 0.958 (external validation). The performance of CNNs falls between experienced and inexperienced pathologists. Grad-CAM and Guided Gard-CAM ensured diagnoses interpretability by highlighting morphological features of malignant changes. CNN is efficient for histological image classification tasks of distinguishing cervical malignancies from benign tissues and could highlight the specific areas of concern. All these findings suggest that CNNs could serve as a diagnostic tool to aid pathologic diagnosis. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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