02876nas a2200229   4500000000100000008004100001260004400042653004100086653001100127653002200138653000900160653000800169653003300177100001500210700001300225700001100238700001400249245015800263490000600421520220500427022001402632       2024                            d  bSpringer Science and Business Media LLC10aLeprosy detection and classification10aResNet10aensemble learning10aGLCM10aCAD10aDeep spatio-textural feature1 aJitendra R1 aSimha JB1 aAbhi S1 aChadha VK00aDeep Semantic Segmentation Assisted Region-of-Interest Sensitive Deep Spatio-Textural Feature Learning Framework for Leprosy Detection and Classification0 v53 a<p>The last few decades have witnessed exponential rise in leprosy also called Hansen diseases globally. Being chronic and infectious in nature, eradicating leprosy has remained a challenge. Despite a few recent efforts employing vision computing-based leprosy detection and classification, most of the at hand solution remain confined due to their inability to address data-imbalance, feature optimality, reliability and scalability. Addressing aforesaid challenges requires a vision-based method to accommodate automated region-of-interest (ROI)-specific information-rich learning and classification. In this paper, a highly robust deep semantic segmentation assisted region-of-interest sensitive deep spatio-textural feature learning framework is proposed for leprosy detection and classification. The proposed method at first applies the different pre-processing methods including contrast adaptive histogram equalization, intensity equalization, resizing. In sync with probable data-imbalance problem, we applied deep semantic segmentation model to obtain the ROI specific regions, which were subsequently processed for RGB conversion. Thus, the obtained ROI-specific RGB images were processed for feature extraction by using deep spatio-textural feature extraction model encompassing GLCM and ResNet101 deep network. Here, GLCM extracted sufficiently large spatio-textural features, while the use of ResNet101 not only provided deep residual features but also alleviated the likelihood of gradient fading problem, which is highly frequent in the state-of-arts like convolutional neural network (CNNs) or other recurrent neural networks (RNNs). Thus, the obtained deep spatio-textual features processed for horizontal concatenation-based feature fusion, which was later trained by using random forest ensemble classifier.The simulation results revealed that the proposed model exhibits accuracy and F-Measure of 97.20% and 97.80%, respectively. Thus, the ability to yield superior accuracy along with the potential to alleviate class-imbalance, feature inferiority and gradient fading makes proposed model more reliable and efficient towards real-time leprosy detection and classification.</p>
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