One of the severe and prolonged disorder of the human brain which disturbs the behavioral characteristics of an individual completely such as interruption in the thinking process and speech is schizophrenia. It is a manifestation of many symptoms such as hallucinations, functional deterioration, disorganized speech and hearing sounds and speeches that are non-existent. In this paper, a computerized approach based on optimization and classification is done to analyze the classification of schizophrenia from Electroencephalography (EEG) signals. As EEG can analyze a lot of brain disorders and is used to study the diseases of the brain in an in-depth manner, it can be used to analyze the schizophrenia EEG signals. In this paper, three feature extraction techniques are employed such as Partial Least Squares (PLS) Non linear Regression technique, Expectation Maximization based Principal Component Analysis (EM-PCA) technique and Isometric Mapping (Isomap) technique. The extracted features are further optimized with four optimization algorithms such as Flower Pollination algorithm, Eagle strategy using different evolution algorithm, Backtracking search optimization algorithm and Group search optimization algorithm. The optimized values are then classified with varied versions of both Adaboost classifier and Naïve Bayesian Classifier. The individual results show that for normal cases, Isomap features when optimized with Backtracking search optimization algorithm and classified with Modest Adaboost classifier, a classification accuracy of 98.77% is obtained. The individual results show that for schizophrenia case, when Isomap features are optimized with Flower Pollination optimization algorithm and classified with Real Adaboost classifier, a classification accuracy of 98.77% is obtained.

Objective: The death rate of breast tumour is falling as there is progress in its research area. However, it is the most common disease among women. It is a great challenge in designing a machine learning model to evaluate the performance of the classification of breast tumour. Methods: Implementing an efficient classification methodology will support in resolving the complications in analyzing breast cancer. This proposed model employs two machine learning (ML) algorithms for the categorization of breast tumour; Decision Tree and K-Nearest Neighbour (KNN) algorithm is used for the breast tumour classification. Result: This classification includes the two levels of disease as benign or malignant. These two machine learning algorithms are verified using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset after feature selection using Principal Component Analysis (PCA). The comparison of these two ML algorithms is done using the standard performance metrics. Conclusion: The comparative analysis results indicate that the KNN classifier outperforms the result of the decision-tree classifier in the breast cancer classification. Asian Pacific Journal of Cancer Prevention (APJCP)

Pests and diseases are the big issues in paddy production and they make the farmers to lose around 20% of rice yield world-wide. Identification of rice leaves diseases at early stage through thermal image cameras will be helpful for avoiding such losses. The objective of this work is to implement a Modified Lemurs Optimization Algorithm as a filter-based feature transformation technique for enhancing the accuracy of detecting various paddy diseases through machine learning techniques by processing the thermal images of paddy leaves. The original Lemurs Optimization is altered through the inspiration of Sine Cosine Optimization for developing the proposed Modified Lemurs Optimization Algorithm. Five paddy diseases namely rice blast, brown leaf spot, leaf folder, hispa, and bacterial leaf blight are considered in this work. A total of six hundred and thirty-six thermal images including healthy paddy and diseased paddy leaves are analysed. Seven statistical features and seven Box-Cox transformed statistical features are extracted from each thermal image and four machine learning techniques namely K-Nearest Neighbor classifier, Random Forest classifier, Linear Discriminant Analysis Classifier, and Histogram Gradient Boosting Classifier are tested. All these classifiers provide balanced accuracy less than 65% and their performance is improved by the usage of feature transform based on Modified Lemurs Optimization. Especially, the balanced accuracy of 90% is achieved by using the proposed feature transform for K-Nearest Neighbor classifier.

Objective: Lung cancer is a type of malignancy that occurs most commonly among men and the third most commontype of malignancy among women. The timely recognition of lung cancer is necessary for decreasing the effect ofdeath rate worldwide. Since the symptoms of lung cancer are identified only at an advanced stage, it is essential topredict the disease at its earlier stage using any medical imaging techniques. This work aims to propose a classificationmethodology for lung cancer automatically at the initial stage. Methods: The work adopts computed tomography (CT)imaging modality of lungs for the examination and probabilistic neural network (PNN) for the classification task.After pre-processing of the input lung images, feature extraction for the work is carried out based on the Gray-LevelCo-Occurrence Matrix (GLCM) and chaotic crow search algorithm (CCSA) based feature selection is proposed.Results: Specificity, Sensitivity, Positive and Negative Predictive Values, Accuracy are the computation metrics used.The results indicate that the CCSA based feature selection effectively provides an accuracy of 90%. Conclusion: Thestrategy for the selection of appropriate extracted features is employed to improve the efficiency of classification andthe work shows that the PNN with CCSA based feature selection gives an improved classification than without usingCCSA for feature selection.

Breast cancer is one of the deadly cancer types that causes high mortality among women globally. Meanwhile, Deep Learning (DL) emerges as the most frequently utilized and rapidly developing branch of classical machine learning. The study examines a modern Computer-Aided Diagnosis (CAD) framework that uses DL to extract features and classify them for aiding radiologists in breast cancer diagnosis. This is accomplished through four distinct experimentations aimed at identifying the most optimal method of effective classification. Here, the first uses Deep CNNs that are pre-trained, such as AlexNet, GoogleNet, ResNet50, and Dense-Net121. The second is based on experiments using Deep CNNs to extract features and applying them onto a Support Vector Machine algorithm using three different kernels. The next one involves the fusion of different deep features for demonstrating the classification improvement by fusion of these deep features. The final experiment involves Principal Component Analysis (PCA) for reducing the computational cost and for decreasing the larger feature vectors created during fusion. The abovesaid experimentations are carried out in two different mammogram datasets namely MIAS and INbreast. The classification accuracy attained for both datasets through the fuzing of deep features (97.93% for MIAS and 96.646% for INbreast) is the highest compared with the state-of-the-art frameworks. In contrast, the classification performance did not enhance while applying the PCA on combined deep features; but the decrease in execution time provides a reduced computational cost. Abbreviations: CAD: Computer Aided Diagnosis; CNN: Convolution Neural Network; CSI: Classification Success Index; DCNN: Deep Convolution Neural Network; DICOM: Digital Imaging and Communications in Medicine; DL: Deep Learning; FC layer: Fully Connected layer; FFDM: Full-Field Digital Mammograms; FN: False Negative; FP: False Positive; ICSI: Individual Classification Success Index; MIAS: Mammographic Image Analysis Society; ML: Machine Learning; MLO: Medio-Lateral Oblique; PCA: Principal Component Analysis; PGM: Portable Gray Map; PPV: Positive Predictive Value; RBF: Radial Basis Function; SGDM: Stochastic Gradient Descent with Momentum; SVM: Support Vector Machine; TN: True Negative; TP: True Positive; TPR: True Positive Rate; UK: United Kingdom