An Automated Classification Technique for COVID-19 Using Optimized Deep Learning Features

In 2020,COVID-19 started spreading throughout the world.This deadly infection was identified as a virus that may affect the lungs and,in severe cases,could be the cause of death.The polymerase chain reaction(PCR)test is commonly used to detect this virus through the nasal passage or throat.However,the PCR test exposes health workers to this deadly virus.To limit human exposure while detecting COVID-19,image processing techniques using deep learning have been successfully applied.In this paper,a strategy based on deep learning is employed to classify the COVID-19 virus.To extract features,two deep learning models have been used,the DenseNet201 and the SqueezeNet.Transfer learning is used in feature extraction,and models are fine-tuned.A publicly available computerized tomography(CT)scan dataset has been used in this study.The extracted features from the deep learning models are optimized using the Ant Colony Optimization algorithm.The proposed technique is validated through multiple evaluation parameters.Several classifiers have been employed to classify the optimized features.The cubic support vector machine(Cubic SVM)classifier shows superiority over other commonly used classifiers and attained an accuracy of 98.72%.The proposed technique achieves state-of-the-art accuracy,a sensitivity of 98.80%,and a specificity of 96.64%.

An Efficient Automated Technique for Classification of Breast Cancer Using Deep Ensemble Model

Breast cancer is one of the leading cancers among women.It has the second-highest mortality rate in women after lung cancer.Timely detection,especially in the early stages,can help increase survival rates.However,manual diagnosis of breast cancer is a tedious and time-consuming process,and the accuracy of detection is reliant on the quality of the images and the radiologist’s experience.However,computer-aided medical diagnosis has recently shown promising results,leading to the need to develop an efficient system that can aid radiologists in diagnosing breast cancer in its early stages.The research presented in this paper is focused on the multi-class classification of breast cancer.The deep transfer learning approach has been utilized to train the deep learning models,and a pre-processing technique has been used to improve the quality of the ultrasound dataset.The proposed technique utilizes two deep learning models,Mobile-NetV2 and DenseNet201,for the composition of the deep ensemble model.Deep learning models are fine-tuned along with hyperparameter tuning to achieve better results.Subsequently,entropy-based feature selection is used.Breast cancer identification using the proposed classification approach was found to attain an accuracy of 97.04%,while the sensitivity and F1 score were 96.87%and 96.76%,respectively.The performance of the proposed model is very effective and outperforms other state-of-the-art techniques presented in the literature.

Classification of Citrus Plant Diseases Using Deep Transfer Learning

In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.

Foliar Application of Phosphorus Enhances Photosynthesis and Biochemical Characteristics of Maize under Drought Stress

Water is essential for the growth period of crops;however,water unavailability badly affects the growth and physiological attributes of crops,which considerably reduced the yield and yield components in crops.Therefore,a pot experiment was conducted to investigate the effect of foliar phosphorus(P)on morphological,gas exchange,biochemical traits,and phosphorus use efficiency(PUE)of maize(Zea mays L.)hybrids grown under normal as well as water deficit situations at the Department of Agronomy,University of Agriculture Faisalabad,Pakistan in 2014.Two different treatments(control and P@8 kg ha^(−1))and four hybrids(Hycorn,31P41,65625,and 32B33)of maize were tested by using a randomized complete block design(RCBD)with three replications.Results showed that the water stress caused a remarkable decline in total soluble protein(9.7%),photosynthetic rate(9.4%)and transpiration rate(13.4%),stomatal conductance(10.2%),and internal CO_(2)rate(20.4%)comparative to well-watered control.An increase of 37.1%,36.8%,and 24.5%were recorded for proline,total soluble sugar,and total free amino acid,respectively.However,foliar P application minimized the negative impact of drought by improving plant growth,physio-biochemical attributes,and PUE in maize plants under water stress conditions.Among the hybrids tested,the hybrid 6525 performed better both under stress and non-stress conditions.These outcomes confirmed that the exogenous application of P improved drought stress tolerance by modulating growth,physio-biochemical attributes,and PUE of maize hybrids.