Mobile-based Intelligent Skin Diseases Diagnosis System. Subtitle as

Mobile-based Intelligent Skin Diseases Diagnosis System

Subtitle as needed (paper subtitle)

Abdulfattah Esmail Hasan Abdullah Ba Alawi

Software Engineering. Faculty of Engineering & IT

Taiz University

Taiz, Yemen

Dr. Amer Ali Sallam

Computer Engineering. Faculty of Engineering & IT

Taiz University

Taiz, Yemen

Abstract Skin diseases are the most common diseases in humans. The inherent variability in the appearance of skin diseases makes it hard even for medical experts to detect disease’s type from dermoscopic images.

Recent advances in image processing using the Convolution Neural Networks have led to better results in diagnosing systems. We aim to develop an advanced diagnosis system in a manner that meets the requirements of real time and extensibility for medical services for skin diseases detection that provides offline detection for the users who have not connection to Internet and online diagnosing service and communication service with a medical team. First, the user capture the affected area and get offline immediate report by on-device diagnosis.

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In addition to online accurate diagnosis option using on-cloud service. The third service is an option to communicate with dermatologists and get medical recommendation. New images that labelled by dermatologists used to retrain the model to enhance system accuracy. As a reason for maximizing the number of users, the system implemented on a mobile-based environment, by the growing numbers of portable apps allows it easy for people to obtain up-to-date data. Users are familiar to looking for answers from the virtual globe, including health issues.

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The following experimental results demonstrate the e?ectiveness and feasibility of the proposed method. Test accuracy is 95.6% for benign cases, for the melanoma case it is 90.78 %.

Keywords Deep Convolution Neural Networks; Virtual globe; dermoscopic; skin lesion;


Skin disease is the most common disease in the globe. It is known as a pathological condition on the body’s surface [1]. It has various appearances and various degree of affects, from slight affect such as changing body characteristics to impact affect like death. Diagnosing skin diseases in early stages is very important due to a high survival possibility especially for skin cancer. That account for around 80% of all newly diagnosed cancers. Early diagnosis of melanoma has an elevated cure rate and a relative survival rate of 99% for 5 years. However, since non-melanoma skin cancer can easily spread to other areas of the body, the comparative 5-year mortality ratio in the long-term falls to 20% [1] or 18% [23]. What show the wide spread of this skin diseases according to American Cancer Society report [4], an estimated 96,480 new cases of melanoma will be diagnosed in 2019. The reason for using mobile-based system no other platform, in the report [5], the number of users of smartphones is expected to reach 7.2 billion in 2024. General diagnostic services can be provided at low costs [6]. Therefore, it is difficult to be classified easily. Therefore, we suggested a method using mobile-based vision techniques to detect different types of skin diseases. In the paper [3] introduced mobile devices that have deep neural network could possibly extend the variety of dermatologists outside the outpatient department. The traditional systems for detecting skin disease complete the classification production by extracting picture information as input features. The research that is currently carried out adopts the deeper framework for automating teaching of features[7-9], with the priority of precision of automatic classification depending on pathological skin dataset being acquired. Meanwhile, the present system is a centralized system that needs an active expert update to include a static and centralized database that limits user mobility and cannot conduct a convenient and effective self-check. Furthermore, the centralized system cannot provide enough resources to support the individualized database for different population groups and cannot make a good judgment of paroxysmal diseases because of centralizing the database [1]. According to the latest World Health Organization (WHO) data published in 2017, skin disease deaths in Yemen reached 166 or 0.11% of total deaths. The age-adjusted death rate is 1.46 per 100,000 of population ranks Yemen #100 in the world [39]. The arrival of smartphones into many fields, for example medical fields, has resulted to the development of new technology to help individuals to identify and diagnose illness precisely and credibly where the doctor’s knowledge, in relation to software precision, leads to exposure to high-patient confidence and saves human life.

In this paper, we developed intelligent mobile-based skin diseases diagnosis system using convolutional neural networks. While skin disease is a global problem, there are many researches in this field. Existed artificial skin diseases diagnosis systems have few solutions available, which are still under research developments. Certain limitations and drawbacks are identified in those hence this work tries overcome the existing problems with different approach [16].

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Mobile-based Intelligent Skin Diseases Diagnosis System. Subtitle as. (2019, Dec 19). Retrieved from

Mobile-based Intelligent Skin Diseases Diagnosis System. Subtitle as

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