Deep Learning Based Automated Diagnosis of Skin Diseases Using Dermoscopy

Biomedical image analysis has been exploited considerably by recent technology involvements,carrying about a pattern shift towards‘automation’and‘error free diagnosis’classification methods with markedly improved accurate diagnosis productivity and cost effectiveness.This paper proposes an automated deep learning model to diagnose skin disease at an early stage by using Dermoscopy images.The proposed model has four convolutional layers,two maxpool layers,one fully connected layer and three dense layers.All the convolutional layers are using the kernel size of 3∗3 whereas the maxpool layer is using the kernel size of 2∗2.The dermoscopy images are taken from the HAM10000 dataset.The proposed model is compared with the three different models of ResNet that are ResNet18,ResNet50 and ResNet101.The models are simulated with 32 batch size and Adadelta optimizer.The proposed model has obtained the best accuracy value of 0.96 whereas the ResNet101 model has obtained 0.90,the ResNet50 has obtained 0.89 and the ResNet18 model has obtained value as 0.86.Therefore,features obtained from the proposed model are more capable for improving the classification performance of multiple skin disease classes.This model can be used for early diagnosis of skin disease and can also act as a second opinion tool for dermatologists.

Deep Learning Based Automated Detection of Diseases from Apple Leaf Images

In Agriculture Sciences, detection of diseases is one of the mostchallenging tasks. The mis-interpretations of plant diseases often lead towrong pesticide selection, resulting in damage of crops. Hence, the automaticrecognition of the diseases at earlier stages is important as well as economicalfor better quality and quantity of fruits. Computer aided detection (CAD)has proven as a supportive tool for disease detection and classification, thusallowing the identification of diseases and reducing the rate of degradationof fruit quality. In this research work, a model based on convolutional neuralnetwork with 19 convolutional layers has been proposed for effective andaccurate classification of Marsonina Coronaria and Apple Scab diseases fromapple leaves. For this, a database of 50,000 images has been acquired bycollecting images of leaves from apple farms of Himachal Pradesh (H.P)and Uttarakhand (India). An augmentation technique has been performedon the dataset to increase the number of images for increasing the accuracy.The performance analysis of the proposed model has been compared with thenew two Convolutional Neural Network (CNN) models having 8 and 9 layersrespectively. The proposed model has also been compared with the standardmachine learning classifiers like support vector machine, k-Nearest Neighbour, Random Forest and Logistic Regression models. From experimentalresults, it has been observed that the proposed model has outperformed theother CNN based models and machine learning models with an accuracy of99.2%.

Early SkinDiseaseIdentification Using Deep Neural Network

Skin lesions detection and classification is a prominent issue and difficult even for extremely skilled dermatologists and pathologists.Skin disease is the most common disorder triggered by fungus,viruses,bacteria,allergies,etc.Skin diseases are most dangerous and may be the cause of serious damage.Therefore,it requires to diagnose it at an earlier stage,but the diagnosis therapy itself is complex and needs advanced laser and photonic therapy.This advance therapy involvesfinancial burden and some other ill effects.Therefore,it must use artificial intelligence techniques to detect and diagnose it accurately at an earlier stage.Several techniques have been proposed to detect skin disease at an earlier stage but fail to get accuracy.Therefore,the primary goal of this paper is to classify,detect and provide accurate information about skin diseases.This paper deals with the same issue by proposing a high-performance Convolution neural network(CNN)to classify and detect skin disease at an earlier stage.The complete meth-odology is explained in different folds:firstly,the skin diseases images are pre-processed with processing techniques,and secondly,the important feature of the skin images are extracted.Thirdly,the pre-processed images are analyzed at different stages using a Deep Convolution Neural Network(DCNN).The approach proposed in this paper is simple,fast,and shows accurate results up to 98%and used to detect six different disease types.