Melanoma Identification Through X-ray Modality Using Inception-v3 Based Convolutional Neural Network

作     者：Saad Awadh Alanazi 

作者机构：College of Computer and Information SciencesJouf UniversitySakakaAljouf72341Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：37-55页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 1002[医学-临床医学] 0809[工学-电子科学与技术（可授工学、理学学位）] 0805[工学-材料科学与工程（可授工学、理学学位）] 100214[医学-肿瘤学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 10[医学] 

基　　金：Thanks to my family and colleagues  who provided moral support 

主　　题：Deep learning chronic obstructive pulmonary disease chronic bronchitis convolutional neural network X-ray images 

摘      要：Melanoma,also called malignant melanoma,is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells,which give the skin its *** is one of the skin diseases,which is exceptionally and globally dangerous,Skin lesions are considered to be a serious ***-based early recognition and detection procedure is fundamental for melanoma *** detection of melanoma using dermoscopy images improves survival rates *** the same time,well-experienced dermatologists dominate the precision of ***,precise melanoma recognition is incredibly hard due to several factors:low contrast between lesions and surrounding skin,visual similarity between melanoma and non-melanoma lesions,and so ***,reliable automatic detection of skin tumors is critical for pathologists’effectiveness and *** take care of this issue,numerous research centers around the world are creating autonomous image processing-oriented *** suggested deep learning methods in this article to address significant tasks that have emerged in the field of skin lesion image processing:we provided a Convolutional Neural Network(CNN)based framework using an Inception-v3(INCP-v3)melanoma detection scheme and accomplished very high precision(98.96%)against melanoma *** classification framework of CNN is created utilizing TensorFlow and Keras in the backend(in Python).It likewise utilizes Transfer-Learning(TL)*** is prepared on the data gathered from the“International Skin Imaging Collaboration(ISIC)*** experiments show that the suggested technique outperforms state-of-the-art methods in terms of predictive performance.