Qian Chen1, Min Li2,3, Chen Chen2,4, Panyun Zhou1, Xiaoyi Lv5,6,7,8, Cheng Chen9. 1. College of Software, Xinjiang University, Urumqi, 830046, China. 2. College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China. 3. Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China. 4. Xinjiang Cloud Computing Application Laboratory, Karamay, 834099, China. 5. College of Software, Xinjiang University, Urumqi, 830046, China. xjuwawj01@163.com. 6. College of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China. xjuwawj01@163.com. 7. Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi, 830046, China. xjuwawj01@163.com. 8. Xinjiang Cloud Computing Application Laboratory, Karamay, 834099, China. xjuwawj01@163.com. 9. College of Software, Xinjiang University, Urumqi, 830046, China. chenchengoptics@gmail.com.
Abstract
PURPOSE: Skin cancer is one of the ten most common cancer types in the world. Early diagnosis and treatment can effectively reduce the mortality of patients. Therefore, it is of great significance to develop an intelligent diagnosis system for skin cancer. According to the survey, at present, most intelligent diagnosis systems of skin cancer only use skin image data, but the multi-modal cross-fusion analysis using image data and patient clinical data is limited. Therefore, to further explore the complementary relationship between image data and patient clinical data, we propose multimode data fusion diagnosis network (MDFNet), a framework for skin cancer based on data fusion strategy. METHODS: MDFNet establishes an effective mapping among heterogeneous data features, effectively fuses clinical skin images and patient clinical data, and effectively solves the problems of feature paucity and insufficient feature richness that only use single-mode data. RESULTS: The experimental results present that our proposed smart skin cancer diagnosis model has an accuracy of 80.42%, which is an improvement of about 9% compared with the model accuracy using only medical images, thus effectively confirming the unique fusion advantages exhibited by MDFNet. CONCLUSIONS: This illustrates that MDFNet can not only be applied as an effective auxiliary diagnostic tool for skin cancer diagnosis, help physicians improve clinical decision-making ability and effectively improve the efficiency of clinical medicine diagnosis, but also its proposed data fusion method fully exerts the advantage of information convergence and has a certain reference value for the intelligent diagnosis of numerous clinical diseases.
PURPOSE: Skin cancer is one of the ten most common cancer types in the world. Early diagnosis and treatment can effectively reduce the mortality of patients. Therefore, it is of great significance to develop an intelligent diagnosis system for skin cancer. According to the survey, at present, most intelligent diagnosis systems of skin cancer only use skin image data, but the multi-modal cross-fusion analysis using image data and patient clinical data is limited. Therefore, to further explore the complementary relationship between image data and patient clinical data, we propose multimode data fusion diagnosis network (MDFNet), a framework for skin cancer based on data fusion strategy. METHODS: MDFNet establishes an effective mapping among heterogeneous data features, effectively fuses clinical skin images and patient clinical data, and effectively solves the problems of feature paucity and insufficient feature richness that only use single-mode data. RESULTS: The experimental results present that our proposed smart skin cancer diagnosis model has an accuracy of 80.42%, which is an improvement of about 9% compared with the model accuracy using only medical images, thus effectively confirming the unique fusion advantages exhibited by MDFNet. CONCLUSIONS: This illustrates that MDFNet can not only be applied as an effective auxiliary diagnostic tool for skin cancer diagnosis, help physicians improve clinical decision-making ability and effectively improve the efficiency of clinical medicine diagnosis, but also its proposed data fusion method fully exerts the advantage of information convergence and has a certain reference value for the intelligent diagnosis of numerous clinical diseases.
Authors: R Corona; E Dogliotti; M D'Errico; F Sera; I Iavarone; G Baliva; L M Chinni; T Gobello; C Mazzanti; P Puddu; P Pasquini Journal: Arch Dermatol Date: 2001-09
Authors: Maris S Jones; Peter C Jones; Stacey L Stern; David Elashoff; Dave S B Hoon; John Thompson; Nicola Mozzillo; Omgo E Nieweg; Dirk Noyes; Harald J Hoekstra; Jonathan S Zager; Daniel F Roses; Alessandro Testori; Brendon J Coventry; Mark B Smithers; Robert Andtbacka; Doreen Agnese; Erwin Schultz; Eddy C Hsueh; Mark Kelley; Schlomo Schneebaum; Lisa Jacobs; Tawnya Bowles; Mohammed Kashani-Sabet; Douglas Johnson; Mark B Faries Journal: Ann Surg Oncol Date: 2017-02-21 Impact factor: 5.344
Authors: F Nachbar; W Stolz; T Merkle; A B Cognetta; T Vogt; M Landthaler; P Bilek; O Braun-Falco; G Plewig Journal: J Am Acad Dermatol Date: 1994-04 Impact factor: 11.527