Rapid, sensitive and accurate tests are critical for contemporary health care. Tuberculosis has been with us since time immemorial, and although being treatable and curable, it remains one of the world’s most infectious malady, killing millions each year. Significant advances have been made in tuberculosis diagnostic technologies however, they are not adequately portable, rendering them inapplicable in remote, rural areas where they are most required. They are costly, lack the essential sensitivity or precision and are frequently susceptible to error. In this regard, an automated system that can analyse and diagnose pulmonary TB by using Chest X-Rays (CXR) can prove to be a cost effective, dependable and quick alternative to manual diagnostic methods. In our work, we propose a customized CNN architecture having three convolution layers and three max pooling layers for accurately classifying the CXRs into TB-infected and normal classes. To deal with the issue of unbalanced classes in the TB CXR dataset, we use different oversampling techniques such as weighted averaging, SMOTE, ADASYN and Borderline SMOTE. To ensure a comprehensive assessment, we extended our research to further employ two widely recognized pre-trained CNNs; DenseNet169 and ResNet50 to gauge their performance on the same task. Grad-CAM has been applied to interpret the decision-making process of the models and correctly identify the regions of interest. Various evaluation metrics employed show very encouraging results. The proposed customized CNN model along with ADASYN outperforms DenseNet and ResNet models in terms of computation time as well.

This paper presents a variant of local binary pattern called Blockwise Binary Pattern (BBP) for the offline signature verification. The proposed approach has three major phases : Preprocessing, Feature extraction and Classification. In the feature extraction phase, the signature is divided into 3 x 3 neighborhood blocks. A BBP value for central pixel of each block is computed by considering its 8 neighboring pixels and the 3 x 3 block is replaced by this central pixel. To compute BBP value for each block, a binary sequence is formed by considering 8 neighbors of the central pixel, by following the pixels in a anti-clockwise direction. Then the minimum decimal equivalent of this binary sequence is computed and this value is assigned to the central pixel. The central pixel is merged with the neighboring 8 pixels representing the 3 X 3 neighborhood block. This method is found to be invariant to rotation, scaling and shift of the signature. The features are stored in the form of normalized histogram. The SVM classifier is used for the signature verification. Experiments have been performed on standard signature datasets namely CEDAR and GPDS which are publicly available English signature datasets and on MUKOS, a regional language (Kannada) dataset and compared with the well-known approaches to exhibit the performance of the proposed approach.

Keywords: Particle swarm optimization, Fuzzy c-means, Taylor’s series expansion, weighted mean Hamming distance, Iris recognition system Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor’s series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.