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.

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.

DWT-SVD Based Image Steganography Using Threshold Value Encryption Method

Digital image steganography technique based on hiding the secret data behind of cover image in such a way that it is not detected by the human visual system.This paper presents an image scrambling method that is very useful for grayscale secret images.In this method,the secret image decomposes in three parts based on the pixel’s threshold value.The division of the color image into three parts is very easy based on the color channel but in the grayscale image,it is difficult to implement.The proposed image scrambling method is implemented in image steganography using discrete wavelet transform(DWT),singular value decomposition(SVD),and sorting function.There is no visual difference between the stego image and the cover image.The extracted secret image is also similar to the original secret image.The proposed algorithm outcome is compared with the existed image steganography techniques.The comparative results show the strength of the proposed technique.

A Post-Processing Algorithm for Boosting Contrast of MRI Images

Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis.State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images.Drastic changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings.To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well.This method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression.After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are computed.Enhanced grey level values are computed from the resultant cumulative histograms.The performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.

A Novel Hybrid Tunicate Swarm Naked Mole-Rat Algorithm for Image Segmentation and Numerical Optimization

This paper provides a new optimization algorithm named as tunicate swarm naked mole-rat algorithm(TSNMRA)which uses hybridization concept of tunicate swarm algorithm(TSA)and naked mole-rat algorithm(NMRA).This newly developed algorithm uses the characteristics of both algorithms(TSA and NMRA)and enhance the exploration abilities of NMRA.Apart from the hybridization concept,important parameter of NMRA such as mating factor is made to be self-adaptive with the help of simulated annealing(sa)mutation operator and there is no need to define its value manually.For evaluating the working capabilities of proposed TSNMRA,it is tested for 100-digit challenge(CEC 2019)test problems and real multi-level image segmentation problem.From the results obtained for CEC 2019 test problems,it can be seen that proposed TSNMRA performs well as compared to original TSA and NMRA.In case of image segmentation problem,comparison of TSNMRA is performed with multi-threshold electro magnetism-like optimization(MTEMO),particle swarm optimization(PSO),genetic algorithm(GA),bacterial foraging(BF)and found superior results for TSNMRA.

Modeling Rules Fission and Modality Selection Using Ontology

Researchers in computer science and computer engineering devote a significant part of their efforts on communication and interaction between man and machine. Indeed, with the advent of multimedia and multimodal processing in real time, the computer is no longer considered only as a computational tool, but as a machine for processing, communication, collection and control. Many machines assist and support many activities in daily life. The main objective of this paper is to propose a new methodological solution by modeling an architecture that facilitates the work of multimodal system especially for a fission module. To realize such systems, we rely on ontology to integrate data semantically. Ontologies provide a structured vocabulary usedas support for data representation. This paper provides a better understanding of the fission system and multimodal interaction. We present our architecture and the description of the detection of optimal modalities. This is done by using an ontological model that contains different applicable scenarios and describes the environment where a multimodal system exists.