Identification of Skin Disease Using Deep Learning

Globally, skin diseases are among the most common health problems in all humans irrespective of age. Prevention and early detection of these diseases can improve the chance of surviving. This model illustrates the identification of skin diseases providing more objective and reliable solutions using deep learning technology and convolutional neural network approach. In this model, the system design, implementation and identification of common skin diseases such as acne, blister, eczema, warts etc. are explained. The system applies deep learning technology to train itself with various images of skin diseases from the Kaggle platform. The accuracy obtained by using deep learning is 83.23%. The main objective of this system is to achieve maximum accuracy of skin disease prediction. Moreover, if the disease is identified the system provides detailed information about the diseases along with home remedies.

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utomated Skin Disease Identification using Deep Learning Algorithm

Biomedical & Pharmacology Journal ◽

10.13005/bpj/1507 ◽

2018 ◽

Vol 11 (3) ◽

pp. 1429-1436 ◽

Cited By ~ 2

Author(s):

Sourav Kumar Patnaik ◽

Mansher Singh Sidhu ◽

Yaagyanika Gehlot ◽

Bhairvi Sharma ◽

P. Muthu

Keyword(s):

Deep Learning ◽

Image Recognition ◽

Skin Disease ◽

Skin Diseases ◽

Learning Algorithm ◽

Disease Prediction ◽

Learning Technology ◽

Deep Learning Algorithm ◽

Tone Color ◽

Dermatological Disorders

Dermatological disorders are one of the most widespread diseases in the world. Despite being common its diagnosis is extremely difficult because of its complexities of skin tone, color, presence of hair. This paper provides an approach to use various computer vision based techniques (deep learning) to automatically predict the various kinds of skin diseases. The system uses three publicly available image recognition architectures namely Inception V3, Inception Resnet V2, Mobile Net with modifications for skin disease application and successfully predicts the skin disease based on maximum voting from the three networks. These models are pretrained to recognize images upto 1000 classes like panda, parrot etc. The architectures are published by image recognition giants for public usage for various applications. The system consists of three phases- The feature extraction phase, the training phase and the testing /validation phase. The system makes use of deep learning technology to train itself with the various skin images. The main objective of this system is to achieve maximum accuracy of skin disease prediction.

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P1-118: QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.121 ◽

2006 ◽

Vol 14 (7S_Part_5) ◽

pp. P315-P316 ◽

Cited By ~ 1

Author(s):

Ye Yuan ◽

Zhangyang Wang ◽

Wendy Lee ◽

Paul VanGilder ◽

Yinghua Chen ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Amyloid Burden ◽

Neural Network Approach

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IC-P-025: FEASIBILITY OF QUANTIFYING AMYLOID BURDEN USING VOLUMETRIC MRI DATA: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.2089 ◽

2006 ◽

Vol 14 (7S_Part_1) ◽

pp. P30-P31

Author(s):

Ye Yuan ◽

Zhangyang Wang ◽

Wendy Lee ◽

Pradeep Thiyyagura ◽

Eric M. Reiman ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Amyloid Burden ◽

Volumetric Mri ◽

Neural Network Approach

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Basic Food Basket Monthly Price of Southern Bahia Cities: A Time Series Forecasting with Deep Learning Using a Recurrent Neural Network Approach

Revista Cereus ◽

10.18605/2175-7275/cereus.v12n4p146-159 ◽

2020 ◽

Vol 12 (4) ◽

pp. 146-159

Author(s):

Murillo A. S. Torres ◽

Mateus S. Marinho ◽

Dany S. Dominguez ◽

Dárcio R. Silva ◽

Hélder Conceição Almeida

Keyword(s):

Neural Network ◽

Time Series ◽

Deep Learning ◽

Recurrent Neural Network ◽

Time Series Forecasting ◽

Network Approach ◽

Neural Network Approach ◽

Southern Bahia ◽

Food Basket

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Fruit Classification Through Deep Learning: A Convolutional Neural Network Approach

Lecture Notes in Electrical Engineering - Communications, Signal Processing, and Systems ◽

10.1007/978-981-13-9409-6_326 ◽

2020 ◽

pp. 2671-2677

Author(s):

Tahir Arshad ◽

Min Jia ◽

Qing Guo ◽

Xuemai Gu ◽

Xiaofeng Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Neural Network Approach

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Deep Learning Neural Network Approach for Predicting the Sorption of Ionizable and Polar Organic Pollutants to a Wide Range of Carbonaceous Materials

Environmental Science & Technology ◽

10.1021/acs.est.9b06287 ◽

2020 ◽

Vol 54 (7) ◽

pp. 4583-4591 ◽

Cited By ~ 15

Author(s):

Gabriel Sigmund ◽

Mehdi Gharasoo ◽

Thorsten Hüffer ◽

Thilo Hofmann

Keyword(s):

Neural Network ◽

Deep Learning ◽

Organic Pollutants ◽

Network Approach ◽

Carbonaceous Materials ◽

Neural Network Approach ◽

Wide Range ◽

Deep Learning Neural Network

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IC-P-026: QUANTIFICATION OF AMYLOID BURDEN FROM FLORBETAPIR PET IMAGES WITHOUT USING TARGET AND REFERENCE REGIONS: PRELIMINARY FINDINGS BASED ON THE DEEP LEARNING 3D CONVOLUTIONAL NEURAL NETWORK APPROACH

Alzheimer s & Dementia ◽

10.1016/j.jalz.2018.06.2090 ◽

2006 ◽

Vol 14 (7S_Part_1) ◽

pp. P31-P31

Author(s):

Ye Yuan ◽

Zhangyang Wang ◽

Wendy Lee ◽

Paul VanGilder ◽

Yinghua Chen ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Approach ◽

Amyloid Burden ◽

Neural Network Approach

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Vision-Based Skin Disease Identification Using Deep Learning

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9391.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 3784-3788

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Skin Disease ◽

Medical Image ◽

Skin Diseases ◽

Learning Algorithm ◽

Disease Prediction ◽

Machine Learning Algorithm ◽

Disease Identification ◽

Different Types

Skin disease is the most common health problems worldwide.Human skin is one of the difficult areas topredict. The difficulty is due to rough areas, irregular skin tones, various factors like burns, moles. We have to identify the diseases excluding these factors.In a developing country like India, it is expensive for a large number of people to go to the dermatologist for their skin disease problem.Every year a large number of population in developing countries like India suffer due to different types of skin diseases. So the need for automatic skin disease prediction is increasing for the patients and as well as the dermatologist. In this paper, a method is proposed that uses computer vision-based techniques to detectvariouskinds of dermatological skin diseases. Inception_v3, Mobilenet, Resnetare three deep learning algorithms used for feature extraction in a medical image and machine learning algorithm namely Logistic Regression is used for training and testing the medical images.Using the combined architecture of the three convolutional neural networks considerable efficiency can be achieved.

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