Computer-Aided Diagnosis for Early Signs of Skin Diseases Using Multi Types Feature Fusion Based on a Hybrid Deep Learning Model

被引:6
|
作者
Almuayqil, Saleh Naif [1 ]
Abd El-Ghany, Sameh [1 ,2 ]
Elmogy, Mohammed [3 ]
机构
[1] Jouf Univ, Coll Comp & Informat Sci, Dept Informat Syst, Sakaka 72388, Al Jouf, Saudi Arabia
[2] Mansoura Univ, Fac Comp & Informat, Dept Informat Syst, Mansoura 35516, Egypt
[3] Mansoura Univ, Fac Comp & Informat, Dept Informat Technol, Mansoura 35516, Egypt
关键词
skin disorders' diagnosis; deep learning techniques; computer-aided diagnosis system; multi-label classification;
D O I
10.3390/electronics11234009
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
According to medical reports and statistics, skin diseases have millions of victims worldwide. These diseases might affect the health and life of patients and increase the costs of healthcare services. Delays in diagnosing such diseases make it difficult to overcome the consequences of these types of disease. Usually, diagnosis is performed using dermoscopic images, where specialists utilize certain measures to produce the results. This approach to diagnosis faces multiple disadvantages, such as overlapping infectious and inflammatory skin diseases and high levels of visual diversity, obstructing accurate diagnosis. Therefore, this article uses medical image analysis and artificial intelligence to present an automatic diagnosis system of different skin lesion categories using dermoscopic images. The addressed diseases are actinic keratoses (solar keratoses), benign keratosis (BKL), melanocytic nevi (NV), basal cell carcinoma (BCC), dermatofibroma (DF), melanoma (MEL), and vascular skin lesions (VASC). The proposed system consists of four main steps: (i) preprocessing the input raw image data and metadata; (ii) feature extraction using six pre-trained deep learning models (i.e., VGG19, InceptionV3, ResNet50, DenseNet201, and Xception); (iii) features concatenation; and (iv) classification/diagnosis using machine learning techniques. The evaluation results showed an average accuracy, sensitivity, specificity, precision, and disc similarity coefficient (DSC) of around 99.94%, 91.48%, 98.82%, 97.01%, and 94.00%, respectively.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Computer-aided diagnosis of retinal diseases using multidomain feature fusion
    Keerthiveena, B.
    Esakkirajan, S.
    Selvakumar, K.
    Yogesh, T.
    INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, 2020, 30 (02) : 367 - 379
  • [2] Computer-Aided Diagnosis of Skin Diseases Using Deep Neural Networks
    Bajwa, Muhammad Naseer
    Muta, Kaoru
    Malik, Muhammad Imran
    Siddiqui, Shoaib Ahmed
    Braun, Stephan Alexander
    Homey, Bernhard
    Dengel, Andreas
    Ahmed, Sheraz
    APPLIED SCIENCES-BASEL, 2020, 10 (07):
  • [3] Computer-Aided Diagnosis of Ophthalmic Diseases Using OCT Based on Deep Learning: A Review
    Zhang, Ruru
    He, Jiawen
    Shi, Shenda
    Kang, Xiaoyang
    Chai, Wenjun
    Lu, Meng
    Liu, Yu
    Haihong, E.
    Ou, Zhonghong
    Song, Meina
    HUMAN CENTERED COMPUTING, 2019, 11956 : 615 - 625
  • [4] Computer-Aided Diagnosis of Spinal Tuberculosis From CT Images Based on Deep Learning With Multimodal Feature Fusion
    Li, Zhaotong
    Wu, Fengliang
    Hong, Fengze
    Gai, Xiaoyan
    Cao, Wenli
    Zhang, Zeru
    Yang, Timin
    Wang, Jiu
    Gao, Song
    Peng, Chao
    FRONTIERS IN MICROBIOLOGY, 2022, 13
  • [5] Computer-aided diagnosis using deep learning in the evaluation of early oesophageal adenocarcinoma
    Ebigbo, Alanna
    Mendel, Robert
    Probst, Andreas
    Manzeneder, Johannes
    de Souza, Luis Antonio, Jr.
    Papa, Joao P.
    Palm, Christoph
    Messmann, Helmut
    GUT, 2019, 68 (07) : 1143 - U222
  • [6] A Computer-Aided Diagnosis System Using Deep Learning for Multiclass Skin Lesion Classification
    Arshad, Mehak
    Khan, Muhammad Attique
    Tariq, Usman
    Armghan, Ammar
    Alenezi, Fayadh
    Javed, Muhammad Younus
    Aslam, Shabnam Mohamed
    Kadry, Seifedine
    COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2021, 2021
  • [7] Computer-aided diagnosis of cystic lung diseases using CT scans and deep learning
    Zhu, Zhibin
    Xing, Wenyu
    Yang, Yanping
    Liu, Xin
    Jiang, Tao
    Zhang, Xingwei
    Song, Yuanlin
    Hou, Dongni
    Ta, Dean
    MEDICAL PHYSICS, 2024, : 5911 - 5926
  • [8] Computer-aided Diagnosis of Four Common Cutaneous Diseases Using Deep Learning Algorithm
    Zhang, Xinyuan
    Wang, Shiqi
    Liu, Jie
    Tao, Cui
    2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2017, : 1304 - 1306
  • [9] Computer-aided diagnosis of cataract using deep transfer learning
    Pratap, Turimerla
    Kokil, Priyanka
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2019, 53
  • [10] Computer-Aided Diagnosis and Localization of Glaucoma Using Deep Learning
    Kim, Mijung
    Park, Ho-min
    Zuallaert, Jasper
    Janssens, Olivier
    Van Hoecke, Sofie
    De Neve, Wesley
    PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM), 2018, : 2357 - 2362