Explainable Machine Learning Techniques in Medical Image Analysis Based on Classification with Feature Extraction

Animals are also afflicted by COVID-19, a virus that is quickly spreading and infects both humans and animals. This fatal viral disease has an impact on people's daily lives, health, and economy of a nation. Most effective machine learning method is deep learning, which offers insightful analysis for examining a significant number of chest x-ray pictures that have a significant bearing on COVID-19 screening. This research proposes novel technique in lung image analysis for detection of lung infection due to COVID using Explainable Machine learning techniques. Here the input has been collected as COVID patient’s lung image dataset and it has been processed for noise removal and smoothening. This processed image features have been extracted using spatio transfer neural network integrated with DenseNet+ architecture. Extracted features has been classified using stacked auto Boltzmann encoder machine with VGG-19Net+. With the transfer learning method integrated into the binary classification process, the suggested algorithm achieves good classification accuracy. The experimental analysis has been carried out for various COVID dataset in terms of accuracy, precision, Recall, F-1score, RMSE, MAP. The proposed technique attained accuracy of 95%, precision of 91%, recall of 85%, F_1 score of 80%, RMSE of 61% and MAP of 51%

Development of wheat lines carrying stem rust resistance gene Sr39 with reduced Aegilops speltoides chromatin and simple PCR markers for marker-assisted selection

The use of major resistance genes is a cost-effective strategy for preventing stem rust epidemics in wheat crops. The stem rust resistance gene Sr39 provides resistance to all currently known pathotypes of Puccinia graminis f. sp. tritici (Pgt) including Ug99 (TTKSK) and was introgressed together with leaf rust resistance gene Lr35 conferring adult plant resistance to P. triticina (Pt), into wheat from Aegilops speltoides. It has not been used extensively in wheat breeding because of the presumed but as yet undocumented negative agronomic effects associated with Ae. speltoides chromatin. This investigation reports the production of a set of recombinants with shortened Ae. speltoides segments through induction of homoeologous recombination between the wheat and the Ae. speltoides chromosome. Simple PCR-based DNA markers were developed for resistant and susceptible genotypes (Sr39#22r and Sr39#50s) and validated across a set of recombinant lines and wheat cultivars. These markers will facilitate the pyramiding of ameliorated sources of Sr39 with other stem rust resistance genes that are effective against the Pgt pathotype TTKSK and its variants.Rohit Mago, P. Zhang, H. S. Bariana, D. C. Verlin, U. K. Bansal, J. G. Ellis and I. S. Dunda