LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,149
تعداد مقالات10,518
تعداد مشاهده مقاله45,415,528
تعداد دریافت فایل اصل مقاله11,291,332
Ameri, A. (1399). A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images. سامانه مدیریت نشریات علمی, 10(6), 801-806. doi: 10.31661/jbpe.v0i0.2004-1107
A Ameri. "A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images". سامانه مدیریت نشریات علمی, 10, 6, 1399, 801-806. doi: 10.31661/jbpe.v0i0.2004-1107
Ameri, A. (1399). 'A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images', سامانه مدیریت نشریات علمی, 10(6), pp. 801-806. doi: 10.31661/jbpe.v0i0.2004-1107
Ameri, A. A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images. سامانه مدیریت نشریات علمی, 1399; 10(6): 801-806. doi: 10.31661/jbpe.v0i0.2004-1107

A Deep Learning Approach to Skin Cancer Detection in Dermoscopy Images

Journal of Biomedical Physics and Engineering
مقاله 60، دوره 10، شماره 6، اسفند 2020، صفحه 801-806    اصل مقاله (684.75 K)
نوع مقاله: Technical Note
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2004-1107
نویسنده
A Ameri*
PhD, Department of Biomedical Engineering, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
چکیده
This work proposes a deep learning model for skin cancer detection from skin lesion images. In this analytic study, from HAM10000 dermoscopy image database, 3400 images were employed including melanoma and non-melanoma lesions. The images comprised 860 melanoma, 327 actinic keratoses and intraepithelial carcinoma (AKIEC), 513 basal cell carcinoma (BCC), 795 melanocytic nevi, 790 benign keratosis, and 115 dermatofibroma cases. A deep convolutional neural network was developed to classify the images into benign and malignant classes. A transfer learning method was leveraged with AlexNet as the pre-trained model. The proposed model takes the raw image as the input and automatically learns useful features from the image for classification. Therefore, it eliminates complex procedures of lesion segmentation and feature extraction. The proposed model achieved an area under the receiver operating characteristic (ROC) curve of 0.91. Using a confidence score threshold of 0.5, a classification accuracy of 84%, the sensitivity of 81%, and specificity of 88% was obtained. The user can change the confidence threshold to adjust sensitivity and specificity if desired. The results indicate the high potential of deep learning for the detection of skin cancer including melanoma and non-melanoma malignancies. The proposed approach can be deployed to assist dermatologists in skin cancer detection. Moreover, it can be applied in smartphones for self-diagnosis of malignant skin lesions. Hence, it may expedite cancer detection that is critical for effective treatment.
کلیدواژه‌ها
Skin Cancer؛ Deep Learning؛ Melanoma؛ Transfer Learning؛ Dermoscopy
سایر فایل های مرتبط با مقاله
مراجع
  1. Xu YG, Aylward JL, Swanson AM, Spiegelman VS, Vanness ER, Teng JM, et al. Nonmelanoma skin cancers: basal cell and squamous cell carcinomas. Abeloff’s Clinical Oncology. 2020:1052-73. doi: 10.1016/B978-0-323-47674-4.00067-0.
  2. Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data. 2018;5:180161. doi: 10.1038/sdata.2018.161. PubMed PMID: 30106392. PubMed PMCID: PMC6091241.
  3. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-8. doi: 10.1038/nature21056. PubMed PMID: 28117445.
  4. Masood A, Ali Al-Jumaily A. Computer aided diagnostic support system for skin cancer: a review of techniques and algorithms. Int J of Biomed Imaging. 2013:323268. doi: 10.1155/2013/323268. PubMed PMID: 24575126. PubMed PMCID: PMC3885227.
  5. Burroni M, Corona R, Dell’Eva G, Sera F, Bono R, Puddu P, et al. Melanoma computer-aided diagnosis: reliability and feasibility study. Clin Cancer Res. 2004;10(6):1881-6. doi: 10.1158/1078-0432.ccr-03-0039. PubMed PMID: 15041702.
  6. Rosado B, Menzies S, Harbauer A, Pehamberger H, Wolff K, Binder M, et al. Accuracy of computer diagnosis of melanoma: a quantitative meta-analysis. Arch of Dermatol. 2003;139(3):361-7. doi: 10.1001/archderm.139.3.361. PubMed PMID: 12622631.
  7. Ameri A. EMG-based wrist gesture recognition using a convolutional neural network. Teh Univ Med J TUMS Publications. 2019;77(7):434-9.
  8. Ameri A, Akhaee MA, Scheme E, Englehart K. Regression convolutional neural network for improved simultaneous EMG control. J Neural Eng. 2019;16(3):036015. doi: 10.1088/1741-2552/ab0e2e. PubMed PMID: 30849774.
  9. Ameri A, Akhaee MA, Scheme E, Englehart K. Real-time, simultaneous myoelectric control using a convolutional neural network. PloS One. 2018;13(9):e0203835. doi: 10.1371/journal.pone.0203835. PubMed PMID: 30212573. PubMed PMCID: PMC6136764
  10. Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Communications of the ACM. 2017;60(6):84-90. doi: 10.1145/3065386.
آمار
تعداد مشاهده مقاله: 11,842
تعداد دریافت فایل اصل مقاله: 1,340


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب