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Malaria disease can indeed be fatal if not identified and treated promptly. Due to advancements in the malaria diagnostic process, microscopy techniques are employed for blood cell analysis. Unfortunately, the diagnostic process of malaria via microscopy depends on microscopic skills. To overcome such issues, machine/deep learning algorithms can be proposed for more accurate and efficient detection of malaria. Therefore, a method is proposed for classifying malaria parasites that consist of three phases. The bilateral filter is applied to enhance image quality. After that shape-based and deep features are extracted. In shape-based pyramid histograms of oriented gradients (PHOG) features are derived with the dimension of N × 300. Deep features are derived from the residual network (ResNet)-50, and ResNet-18 at fully connected layers having the dimension of N × 1,000 respectively. The features obtained are fused serially, resulting in a dimensionality of N × 2,300. From this set, N × 498 features are chosen using the generalized normal distribution optimization (GNDO) method. The proposed method is accessed on a microscopic malarial parasite imaging dataset providing 99% classification accuracy which is better than as compared to recently published work. [ABSTRACT FROM AUTHOR]

Worldwide cotton is the most profitable cash crop. Each year the production of this crop suffers because of several diseases. At an early stage, computerized methods are used for disease detection that may reduce the loss in the production of cotton. Although several methods are proposed for the detection of cotton diseases, however, still there are limitations because of low-quality images, size, shape, variations in orientation, and complex background. Due to these factors, there is a need for novel methods for features extraction/selection for the accurate cotton disease classification. Therefore in this research, an optimized features fusion-based model is proposed, in which two pre-trained architectures called EfficientNet-b0 and Inception-v3 are utilized to extract features, each model extracts the feature vector of length N × 1000. After that, the extracted features are serially concatenated having a feature vector lengthN×2000. Themost prominent features are selected usingEmperor PenguinOptimizer (EPO) method. The method is evaluated on two publically available datasets, such as Kaggle cotton disease dataset-I, and Kaggle cotton-leaf-infection-II. The EPO method returns the feature vector of length 1 × 755, and 1 × 824 using dataset-I, and dataset-II, respectively. The classification is performed using 5, 7, and 10 folds cross-validation. The Quadratic Discriminant Analysis (QDA) classifier provides an accuracy of 98.9% on 5 fold, 98.96% on 7 fold, and 99.07% on 10 fold using Kaggle cotton disease dataset-I while the Ensemble Subspace K Nearest Neighbor (KNN) provides 99.16% on 5 fold, 98.99% on 7 fold, and 99.27% on 10 fold using Kaggle cotton-leaf-infection dataset-II. [ABSTRACT FROM AUTHOR]

Summary: COVID‐19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID‐19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre‐trained models like AlexNet and MobileNet in phase‐I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase‐II, quantum transfer learning model is utilized, in which a pre‐trained ResNet‐18 model is applied for DF collection and then these features are supplied as an input to the 4‐qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x‐ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus‐positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it. [ABSTRACT FROM AUTHOR]

As they have nutritional, therapeutic, so values, plants were regarded as important and they’re the main source of humankind’s energy supply. Plant pathogens will affect its leaves at a certain time during crop cultivation, leading to substantial harm to crop productivity & economic selling price. In the agriculture industry, the identification of fungal diseases plays a vital role. However, it requires immense labor, greater planning time, and extensive knowledge of plant pathogens. Computerized approaches are developed and tested by different researchers to classify plant disease identification, and that in many cases they have also had important results several times. Therefore, the proposed study presents a new framework for the recognition of fruits and vegetable diseases. This work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once (YOLO)v2 and Open Exchange Neural (ONNX)model. The localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 model. The localized images passed as input to classify the different types of plant diseases. The classification model is constructed by ensembling the deep features learning, where features are extracted dimension of 1 × 1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input, 01 ReLU, 01 Batch-normalization, 02 fully-connected. The proposed model classifies the plant input images into associated labels with approximately 95% prediction scores that are far better as compared to current published work in this domain. [ABSTRACT FROM AUTHOR]

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID‐19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID‐19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three‐phase model is proposed for COVID‐19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet‐18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto‐encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification. [ABSTRACT FROM AUTHOR]

White blood cells (WBCs) are a vital part of the immune system that protect the body from different types of bacteria and viruses. Abnormal cell growth destroys the body's immune system, and computerized methods play a vital role in detecting abnormalities at the initial stage. In this research, a deep learning technique is proposed for the detection of leukemia. The proposed methodology consists of three phases. Phase I uses an open neural network exchange (ONNX) and YOLOv2 to localize WBCs. The localized images are passed to Phase II, in which 3D-segmentation is performed using deeplabv3 as a base network of the pre-trained Xception model. The segmented images are used in Phase III, in which features are extracted using the darknet-53 model and optimized using Bhattacharyya separately criteria to classify WBCs. The proposed methodology is validated on three publically available benchmark datasets, namely ALL-IDB1, ALL-IDB2, and LISC, in terms of differentmetrics, such as precision, accuracy, sensitivity, and dice scores. The results of the proposed method are comparable to those of recent existing methodologies, thus proving its effectiveness. [ABSTRACT FROM AUTHOR]

Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance. [ABSTRACT FROM AUTHOR]

COVID-19 is a quickly spreading over 10 million persons globally. The overall number of infected patients worldwide is estimated to be around 133,381,413 people. Infection rate is being increased on daily basis. It has also caused a devastating effect on the world economy and public health. Early stage detection of this disease is mandatory to reduce the mortality rate. Artificial intelligence performs a vital role for COVID-19 detection at an initial stage using chest radiographs. The proposed methods comprise of the two phases. Deep features (DFs) are derived from its last fully connected layers of pre-trained models like AlexNet and MobileNet in phase-I. Later these feature vectors are fused serially. Best features are selected through feature selection method of PCA and passed to the SVM and KNN for classification. In phase-II, quantum transfer learning model is utilized, in which a pre-trained ResNet-18 model is applied for DF collection and then these features are supplied as an input to the 4-qubit quantum circuit for model training with the tuned hyperparameters. The proposed technique is evaluated on two publicly available x-ray imaging datasets. The proposed methodology achieved an accuracy index of 99.0% with three classes including corona virus-positive images, normal images, and pneumonia radiographs. In comparison to other recently published work, the experimental findings show that the proposed approach outperforms it., (© 2021 John Wiley & Sons Ltd.)