A Multi-Stage Faster RCNN-Based iSPLInception for Skin Disease Classification Using Novel Optimization.
Nowadays, skin cancer is considered a serious disorder in which early identification and treatment of the disease are essential to ensure the stability of the patients. Several existing skin cancer detection methods are introduced by employing deep learning (DL) to perform skin disease classificatio...
| Publicado en: | Journal of Digital Imaging Vol. 36; no. 5; pp. 2210 - 2227 |
|---|---|
| Autores principales: | , , , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2023
|
| 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=171950857&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 171950857 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2023 vid: 36 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 171950857 164322373 171950857 171950857 10.1007/s10278-023-00848-3 171950857 ppf: 2210 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Multi-Stage Faster RCNN-Based iSPLInception for Skin Disease Classification Using Novel Optimization. aug: au: Josphineleela, R. Raja Rao, P. B. V. shaikh, Amir Sudhakar, K. affil: Department of Computer Science and Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India sug: subj: Skin Diseases Classification Skin Neoplasms Diagnosis Signal Processing, Computer Assisted Methods Neural Networks (Computer) Methods Animal Studies Rodents Descriptive Statistics Data Analysis Software Analysis of Variance Comparative Studies Friedman Test ab: Nowadays, skin cancer is considered a serious disorder in which early identification and treatment of the disease are essential to ensure the stability of the patients. Several existing skin cancer detection methods are introduced by employing deep learning (DL) to perform skin disease classification. Convolutional neural networks (CNNs) can classify melanoma skin cancer images. But, it suffers from an overfitting problem. Therefore, to overcome this problem and to classify both benign and malignant tumors efficiently, the multi-stage faster RCNN-based iSPLInception (MFRCNN-iSPLI) method is proposed. Then, the test dataset is used for evaluating the proposed model performance. The faster RCNN is employed directly to perform image classification. This may heavily raise computation time and network complications. So, the iSPLInception model is applied in the multi-stage classification. In this, the iSPLInception model is formulated using the Inception-ResNet design. For candidate box deletion, the prairie dog optimization algorithm is utilized. We have utilized two skin disease datasets, namely, ISIC 2019 Skin lesion image classification and the HAM10000 dataset for conducting experimental results. The methods' accuracy, precision, recall, and F1 score values are calculated, and the results are compared with the existing methods such as CNN, hybrid DL, Inception v3, and VGG19. With 95.82% accuracy, 96.85% precision, 96.52% recall, and 0.95% F1 score values, the output analysis of each measure verified the prediction and classification effectiveness of the method. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|