This paper implemented a new skin lesion detection method based on the genetic algorithm (GA) for optimizing the neutrosophic set (NS) operation to reduce the indeterminacy on the dermoscopy images. Then, k-means clustering is applied to segment the skin lesion regions. Therefore, the proposed method is called optimized neutrosophic k-means (ONKM). On the training images set, an initial value of α in the α-mean operation of the NS is used with the GA to determine the optimized α value. The Jaccard index is used as the fitness function during the optimization process. The GA found the optimal α in the α-mean operation as α optimal = 0.0014 in the NS, which achieved the best performance using five fold cross-validation. Afterward, the dermoscopy images are transformed into the neutrosophic domain via three memberships, namely true, indeterminate, and false, using α optimal. The proposed ONKM method is carried out to segment the dermoscopy images. Different random subsets of 50 images from the ISIC 2016 challenge dataset are used from the training dataset during the fivefold cross-validation to train the proposed system and determine α optimal. Several evaluation metrics, namely the Dice coefficient, specificity, sensitivity, and accuracy, are measured for performance evaluation of the test images using the proposed ONKM method with α optimal = 0.0014 compared to the k-means, and the γ-k-means methods. The results depicted the dominance of the ONKM method with 99.29 ± 1.61% average accuracy compared with k-means and γ-k-means methods.

Schistosomiasis is one of the dangerous parasitic diseases that affect the liver tissues leading to liver fibrosis. Such disease has several levels, which indicate the degree of fibrosis severity. To assess the fibrosis level for diagnosis and treatment, the microscopic images of the liver tissues were examined at their different stages. In the present work, an automated staging method is proposed to classify the statistical extracted features from each fibrosis stage using an ensemble classifier, namely the subspace ensemble using linear discriminant learning scheme. The performance of the subspace/discriminant ensemble classifier was compared to other ensemble combinations, namely the boosted/ trees ensemble, bagged/trees ensemble, subspace/KNN ensemble, and the RUSBoosted/trees ensemble. The simulation results established the superiority of the proposed subspace/discriminant ensemble with 90% accuracy compared to the other ensemble classifiers.

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Hepatic schistosomiasis is a prolonged disease resulting mainly from the solvable egg antigen of schistosomiasis infection due to the host's granulomatous cell-mediated immune. Irreversible fibrosis results from the progress of the schistosomal hepatopathy. Sensitive diagnosis of this disease is based on the investigation of the microscopy images, liver tissues, and egg identification. Early diagnosis of schistosomiasis at its initial infection stage is vital to avoid egg-induced irreparable pathological reactions. Typically, there are several classification approaches that can be used for liver fibrosis staging. However, it is unclear which approaches can achieve high accuracy for analyzing and intelligently classifying the liver microscopic images. Consequently, this work aims to study the performance of the different machine learning classifiers for accurate fibrosis level staging of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The classifiers include a multi-layer perceptron neural network, a decision tree, discriminant analysis, support vector machine (SVM), nearest neighbor, and the ensemble of classifiers. The statistical features of the microscopic images are extracted from the different fibrosis levels of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The results established that the maximum achieved classification accuracies of value 90% were achieved using the subspace discriminant ensemble, the quadratic SVM, the linear SVM, or the linear discriminant classifiers. However, the linear discriminant classifier can be considered the superior classifier as it realized the best area under the curve of value 0.96 during the classification of the cellular granuloma as well as the fibro-cellular granuloma fibrosis levels.

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