Search

A brain tumor is an abnormal enlargement of cells if not properly diagnosed. Early detection of a brain tumor is critical for clinical practice and survival rates. Brain tumors arise in a variety of shapes, sizes, and features, with variable treatment options. Manual detection of tumors is difficult, time-consuming, and error-prone. Therefore, a significant requirement for computerized diagnostics systems for accurate brain tumor detection is present. In this research, deep features are extracted from the inceptionv3 model, in which score vector is acquired from softmax and supplied to the quantum variational classifier (QVR) for discrimination between glioma, meningioma, no tumor, and pituitary tumor. The classified tumor images have been passed to the proposed Seg-network where the actual infected region is segmented to analyze the tumor severity level. The outcomes of the reported research have been evaluated on three benchmark datasets such as Kaggle, 2020-BRATS, and local collected images. The model achieved greater than 90% detection scores to prove the proposed model's effectiveness.

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., Denoise convolutional neural network regression model used for noise removal to enhance images quality. Model deeplabv3 is used as a backbone of the ResNet‐18 model to segment infected lungs region. Segmented images are further supplied to stack sparse autoencoder model for COVID‐19 classification.

Concerns related to current harmonics have gained increased attention in recent years due to technological advancement in power electronics and the proliferation of non-linear loads in power distribution system. It has become challenging to limit current total harmonic distortion (THD) at consumer end mainly due to increased penetration of intermittent switching based non-linear loads. Shunt active power filter (SAPF) is considered a marvelous power electronics device to tackle consumer side current harmonics. This paper presents a synchronous reference frame (SRF) based improved control method for compliant working of SAPF to reduce current harmonics in the distribution network with same and different THD levels in three phases. This research work investigates an effectual DC link voltage control technique linked with SRF based control of SAPF to effectively mitigate current harmonics. A fuzzy logic-based automatic switch (FLBAS) is designed according to the THD standards of IET and IEEE for real-time controlling of SRF based control of SAPF. In addition, S-plane stability analysis of the proposed control scheme is performed using the model of SAPF's inverter. It is established that the closed-loop system is asymptotically stable with the proposed control scheme. The presented simulation results validate the effectiveness of the proposed control technique for SAPF robustly.

White blood cells (WBC) are an important component of immune mechanism, as they protect human body from parasites, viruses, fungi, and bacteria. The number of blood cells provides significant information related to infections such as AIDS, leukemia, deficiencies of immune and autoimmune infections. To heal an infection in a timely manner, it is critical to recognize it early on. Therefore, a method is proposed to accurately segment and classify WBC at an early stage. The RGB image is converted into HSV after which dual thresholding is applied to the saturation component to segment WBC. The 1000 features are extracted from Alexnet to FC8 layer, Logits layer is selected for feature extraction from mobilenetv2, node_202 layer is utilized to extract the features from shuffle net and FC1000 layer is chosen from Resnet-18 model. Four feature vectors are obtained from transfer learning models; these feature vectors are combined serially and create the final optimized vector by non-dominated sorting genetic algorithm (NSGA). The classification results are investigated on the fusion of Alexnet, shuffle net, Resnet-18, mobilenetv2 and the fusion of mobilenetv2, shuffle net and Resnet-18 whereas mobilenetv2 features are fused independently. The method is tested on three publicly available datasets such as LISC, ALL_IDB1, and ALL_IDB2. The method achieved maximum 1.00 accuracy to classify the blast/non-blast cells, 0.9992 accuracy on Basophil cells, and 1.00 accuracy on Lymphocyte, Neutrophil, Monocyte, Eosinophil, and mixture of these cells. When compared to existing modern approaches, the proposed method produces better outcomes.

Coronavirus 19 (COVID-19) can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing, segmentation, feature extraction/ fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase,COVID-19 lesions are segmented using an encoder- /decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification head. DeepLabv3 is an effective decoder that helps to refine segmentation of lesion boundaries. The model was trained using fine-tuned hyperparameters selected after extensive experimentation. Subsequently, the Gray Level Co-occurrence Matrix (GLCM) features and statistical features including circularity, area, and perimeters were computed for each segmented image. The computed features were serially fused and the best features (those that were optimally discriminatory) selected using a Genetic Algorithm (GA) for classification. The performance of the method was evaluated using two benchmark datasets: The COVID-19 Segmentation and the POF Hospital datasets. The results were better than those of existing methods. © 2021 Tech Science Press. All rights reserved.

Dynamic voltage restorer (DVR) is a power electronics based custom power device (CPD) which is used in distribution system to mitigate voltage sag problem, although the performance of DVR depends mainly on its control technique. In this paper, a synchronous reference frame (SRF) theory based versatile control technique is presented for DVR to mitigate voltage sag problem for sensitive loads in distribution system. The DVR is connected with the load bus in series through injection transformer and filter combination. To improve sag detection in DVR a fuzzy logic based automatic switch is introduced. Proposed control technique has capability to settle balanced as well as imbalanced sag. An efficient strategy to regulate DC link capacitor voltage of DVR is also presented in this paper. Performance of DVR with the proposed control technique is evaluated using Matlab/Simulink. Simulation results show the effectiveness of proposed control technique for DVR to settle voltage sag.

The common fatal type of skin cancer is melanoma. Recently, numerous intelligent systems are used to detect skin cancer at an early stage. These systems are helpful for a dermatologist as a preliminary judgment to diagnose skin cancer. However, accurate skin lesion detection is an intricate task. This work comprises three main phases, firstly perform preprocessing to resize the images to 240 × 240 × 3 and convert RGB into L^* a^* b^* in which the luminance channel is selected. Secondly, Biorthogonal 2-D wavelet transform, Otsu algorithm are used to segment the skin lesion. Thirdly, deep features extracted from pre-trained Alex net and VGG16 and serially fused. The applied PCA for optimal features selection for classification into benign and malignant. The publically available datasets (PH2, ISBI 2016- 2017) are merged to form a single large dataset for the validated of proposed method. The results comparison is performed with the existing work which confirms that the proposed method classifies the skin lesion more accurately.

Worldwide, more than 1.5 million deaths are occur due to liver cancer every year. The use of computed tomography (CT) for early detection of liver cancer could save millions of lives per year. There is also an urgent need for a computerized method to interpret, detect and analyze CT scans reliably, easily, and correctly. However, precise segmentation of minute tumors is a difficult task because of variation in the shape, intensity, size, low contrast of the tumor, and the adjacent tissues of the liver. To address these concerns, a model comprised of three parts: synthetic image generation, localization, and segmentation, is proposed. An optimized generative adversarial network (GAN) is utilized for generation of synthetic images. The generated images are localized by using the improved localization model, in which deep features are extracted from pre-trained Resnet-50 models and fed into a YOLOv3 detector as an input. The proposed modified model localizes and classifies the minute liver tumor with 0.99 mean average precision (mAp). The third part is segmentation, in which pre-trained Inceptionresnetv2 employed as a base-Network of Deeplabv3 and subsequently is trained on fine-tuned parameters with annotated ground masks. The experiments reflect that the proposed approach has achieved greater than 95% accuracy in the testing phase and it is proven that, in comparison to the recently published work in this domain, this research has localized and segmented the liver and minute liver tumor with more accuracy.

Pests are always the main source of field damage and severe crop output losses in agriculture. Currently, manually classifying and counting pests is time consuming, and enumeration of population accuracy might be affected by a variety of subjective measures. Additionally, due to pests’ various scales and behaviors, the current pest localization algorithms based on CNN are unsuitable for effective pest management in agriculture. To overcome the existing challenges, in this study, a method is developed for the localization and classification of pests. For localization purposes, the YOLOv5 is trained using the optimal learning hyperparameters which more accurately localize the pest region in plant images with 0.93 F1 scores. After localization, pest images are classified into Paddy with pest/Paddy without pest using the proposed quantum machine learning model, which consists of fifteen layers with two-qubit nodes. The proposed network is trained from scratch with optimal parameters that provide 99.9% classification accuracy. The achieved results are compared to the existing recent methods, which are performed on the same datasets to prove the novelty of the developed model.