Melanoma Diagnosis Using Enhanced Faster Region Convolutional Neural Networks Optimized by Artificial Gorilla Troops Algorithm

被引:9
作者
Nivedha, S. [1 ]
Shankar, S. [2 ]
机构
[1] Anna Univ, Informat & Commun Engn, Chennai 600025, Tamil Nadu, India
[2] Hindusthan Coll Engn & Technol, Dept Comp Sci & Engn, Coimbatore 641050, Tamil Nadu, India
来源
INFORMATION TECHNOLOGY AND CONTROL | 2023年 / 52卷 / 04期
关键词
Deep learning; Skin Melanoma; Faster R-CNN; African Gorilla Troops Optimizer; Convolutional Neural Networks; SKIN-CANCER; CLASSIFICATION;
D O I
10.5755/j01.itc.52.4.33503
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Melanoma, a rapidly spreading and perilous type of skin cancer, is the focus of this study, presenting a reliable technique for its detection. It is one of the most prevalent types of cancer that might be challenging for medical professionals to diagnose. Artificial intelligence can improve diagnostic accuracy when utilized in conjunction with the expertise of medical specialists. An innovative computer-aided method for the diagnosis of skin cancer has been introduced in the current study. The construction of the proposed method uses the African Gorilla Troops Optimizer (AGTO) Algorithm, a recently introduced meta-heuristic optimization algorithm, and deep learning models such as Faster Region Convolutional Neural Networks. To reduce the complexity of the analytic process, valuable features are chosen using the AGTO method, and further classification is implemented using Faster R-CNN. The proposed model is applied to the ISIC-2020 skin cancer dataset. When the final performance results from the proposed model are compared to those from four existing works, the findings show that the proposed system outperforms the existing models with an accuracy of 98.55%.
引用
收藏
页码:819 / 832
页数:14
相关论文
共 29 条
  • [1] Malignant skin melanoma detection using image augmentation by oversampling in nonlinear lower-dimensional embedding manifold
    Abayomi-Alli, Olusola Oluwakemi
    Damasevicius, Robertas
    Misra, Sanjay
    Maskeliunas, Rytis
    Abayomi-Alli, Adebayo
    [J]. TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2021, 29 : 2600 - 2614
  • [2] Skin Lesion Classification Using Convolutional Neural Network With Novel Regularizer
    Albahar, Marwan Ali
    [J]. IEEE ACCESS, 2019, 7 : 38306 - 38313
  • [3] Melanoma and Nevus Skin Lesion Classification Using Handcraft and Deep Learning Feature Fusion via Mutual Information Measures
    Almaraz-Damian, Jose-Agustin
    Ponomaryov, Volodymyr
    Sadovnychiy, Sergiy
    Castillejos-Fernandez, Heydy
    [J]. ENTROPY, 2020, 22 (04)
  • [4] Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour
    Amali, D. Geraldine Bessie
    Dinakaran, M.
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2019, 37 (06) : 8063 - 8076
  • [5] Angurana N., 2019, International Journal of Engineering and Management Research, V9, DOI [10.31033/ijemr.9.2.13, DOI 10.31033/IJEMR.9.2.13]
  • [6] Melanoma diagnosed on digital dermoscopy monitoring: A side-by-side image comparison is needed to improve early detection
    Babino, Graziella
    Lallas, Aimilios
    Agozzino, Marina
    Alfano, Roberto
    Apalla, Zoe
    Brancaccio, Gabriella
    Giorgio, Caterina M.
    Fulgione, Elisabetta
    Kittler, Harald
    Kyrgidis, Athanassios
    Papageorgiou, Chryssuola
    Argenziano, Giuseppe
    [J]. JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY, 2021, 85 (03) : 619 - 625
  • [7] Automatic segmentation of optic disc in retinal fundus images using semi-supervised deep learning
    Bengani, Shaleen
    Jothi, Angel Arul J.
    Vadivel, S.
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (03) : 3443 - 3468
  • [8] Deep neural networks are superior to dermatologists in melanoma image classification
    Brinker, Titus J.
    Hekler, Achim
    Enk, Alexander H.
    Berking, Carola
    Haferkamp, Sebastian
    Hauschild, Axel
    Weichenthal, Michael
    Klode, Joachim
    Schadendorf, Dirk
    Holland-Letz, Tim
    von Kalle, Christof
    Froehling, Stefan
    Schilling, Bastian
    Utikal, Jochen S.
    [J]. EUROPEAN JOURNAL OF CANCER, 2019, 119 : 11 - 17
  • [9] Deep learning ensembles for melanoma recognition in dermoscopy images
    Codella, N. C. F.
    Nguyen, Q. -B.
    Pankanti, S.
    Gutman, D. A.
    Helba, B.
    Halpern, A. C.
    Smith, J. R.
    [J]. IBM JOURNAL OF RESEARCH AND DEVELOPMENT, 2017, 61 (4-5)
  • [10] The skin cancer classification using deep convolutional neural network
    Dorj, Ulzii-Orshikh
    Lee, Keun-Kwang
    Choi, Jae-Young
    Lee, Malrey
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2018, 77 (08) : 9909 - 9924