Melanoma Detection Based on Hybridization of Extended Feature Space

Melanoma is a perfidious form of skin cancer.The study offers a hybrid framework for the automatic classification of melanoma.An Auto-matic Melanoma Detection System(AMDS)is used for identifying melanoma from the infected area of the skin image using image processing techniques.A larger number of pre-existing automatic melanoma detection systems are either commercial or their accuracy can be further improved.The research problem is to identify the best preprocessing technique,feature extractor,and classifier for melanoma detection using publically available MED-NODE data set.AMDS goes through four stages.The preprocessing stage is for noise removal;the segmentation stage is for extracting lesions from infected skin images;the feature extraction stage is for determining the features like asymmetry,border,and color,and the classification stage is to classify the lesion as benign or melanoma.The infected input image for the AMDS may contain impurities such as noise,illumination,artifacts,and hairs.In the proposed methodology an algorithm LePrePro is proposed for the prepro-cessing stage for denoising and brightness cum contrast normalization and another algorithm LeFET is proposed for extending the feature vector space in the feature extraction stage using a hybrid approach.In the study,a novel approach has been proposed in which different classifiers,feature extractions,and data preprocessing steps of the AMDS are compared.In a conclusion,this comparison revealed that on experimentation using Med-Node and ISIC 2017 Dataset,the best results included Gaussian blur as the best data preprocessing step,Extended feature vector which is the combination of Hue Saturation Value(HSV),and Local Binary Pattern(LBP)was the best feature extraction method,and the ensemble bagged tree was the best classification technique on the Med-Node data sets with 99%Area Under the Receiver Operating Characteristic Curve(AUC),93.52%accuracy,90.82%sensitivity,and 98.36%specificity in the proposed automatic melanoma detection system.

Experimental study of laser cladding process and prediction of process parameters by artificial neural network(ANN)

Laser cladding of powder mixture of TiN and SS304 is carried out on an SS304 substrate with the help of fibre laser.The experiments are performed on SS304,as per the Taguchi orthogonal array(L^(16))by different combinations of controllable parameters(microhardness and clad thickness).The microhardness and clad thickness are recorded at all the experimental runs and studied using Taguchi S/N ratio and the optimum controllable parametric combination is obtained.However,an artificial neural network(ANN)identifies different sets of optimal combinations from Taguchi method but they both got almost the same clad thickness and hardness values.The micro-hardness of cladded layer is found to be6.22 times(HV_(0.5)752)the SS304 hardness(HV_(0.5)121).The presence of nitride ceramics results in a higher micro hardness.The cladded surface is free from cracks and pores.The average clad thickness is found to be around 0.6 mm.

Feature selection for face recognition:a memetic algorithmic approach

The eigenface method that uses principal component analysis(PCA) has been the standard and popular method used in face recognition.This paper presents a PCA-memetic algorithm(PCA-MA) approach for feature selection.PCA has been extended by MAs where the former was used for feature extraction/dimensionality reduction and the latter exploited for feature selection.Simulations were performed over ORL and YaleB face databases using Euclidean norm as the classifier.It was found that as far as the recognition rate is concerned,PCA-MA completely outperforms the eigenface method.We compared the performance of PCA extended with genetic algorithm(PCA-GA) with our proposed PCA-MA method.The results also clearly established the supremacy of the PCA-MA method over the PCA-GA method.We further extended linear discriminant analysis(LDA) and kernel principal component analysis(KPCA) approaches with the MA and observed significant improvement in recognition rate with fewer features.This paper also compares the performance of PCA-MA,LDA-MA and KPCA-MA approaches.