•Automated method is proposed for detection and classification of tumors using MRI at the image and lesion levels.•Different techniques have been applied for the segmentation of candidate lesion.•A ...hybrid features set is selected for grade identification.•Three variants of SVM are tested with different cross validations on the features set to compare precision.•The proposed method is validated on two publicly available datasets such as Harvard, RIDER and one local dataset.
A very exigent task for radiologists is early brain tumor detection. Brain tumor raises very fast, its average size doubles in just twenty-five days. If not treated properly, the survival rate of the patient is normally not more than half a year. It can rapidly lead to death. For this reason, an automatic system is required for brain tumor detection at an early stage. In this paper, an automated method is proposed to easily differentiate between cancerous and non-cancerous Magnetic Resonance Imaging (MRI) of the brain. Different techniques have been applied for the segmentation of candidate lesion. Then a features set is chosen for every applicant lesion using shape, texture, and intensity. At that point, Support Vector Machine (SVM) classifier is applied with different cross validations on the features set to compare the precision of proposed framework. The proposed method is validated on three benchmark datasets such as Harvard, RIDER and Local. The method achieved average 97.1% accuracy, 0.98 area under curve, 91.9% sensitivity and 98.0% specificity. It can be used to identify the tumor more accurately in less processing time as compared to existing methods.
•A contrast stretching technique is proposed to enhance the contrast of infected region.•Construction of a codebook using an improved texture, color, and geometric features.•Implement a feature ...selection technique based on PCA, skewness, and entropy.•Preparing the database of diseases images for citrus leaves.
In agriculture, plant diseases are primarily responsible for the reduction in production which causes economic losses. In plants, citrus is used as a major source of nutrients like vitamin C throughout the world. However, ‘Citrus’ diseases badly effect the production and quality of citrus fruits. From last decade, the computer vision and image processing techniques have been widely used for detection and classification of diseases in plants. In this article, we propose a hybrid method for detection and classification of diseases in citrus plants. The proposed method consists of two primary phases; (a) detection of lesion spot on the citrus fruits and leaves; (b) classification of citrus diseases. The citrus lesion spots are extracted by an optimized weighted segmentation method, which is performed on an enhanced input image. Then, color, texture, and geometric features are fused in a codebook. Furthermore, the best features are selected by implementing a hybrid feature selection method, which consists of PCA score, entropy, and skewness-based covariance vector. The selected features are fed to Multi-Class Support Vector Machine (M-SVM) for final citrus disease classification. The proposed technique is tested on Citrus Disease Image Gallery Dataset, Combined dataset (Plant Village and Citrus Images Database of Infested with Scale), and our own collected images database. We used these datasets for detection and classification of citrus diseases namely anthracnose, black spot, canker, scab, greening, and melanose. The proposed technique outperforms the existing methods and achieves 97% classification accuracy on citrus disease image gallery dataset, 89% on combined dataset and 90.4% on our local dataset.
•Discussed challenges for detection and classification of citrus plant diseases.•Briefly explains recent studies including segmentation and classification.•Compare this review with existing state of ...the arts.•Discussed the advantages and drawbacks of each step with detail.
The citrus plants such as lemons, mandarins, oranges, tangerines, grapefruits, and limes are commonly grown fruits all over the world. The citrus producing companies create a large amount of waste every year whereby 50% of citrus peel is destroyed every year due to different plant diseases. This paper presents a survey on the different methods relevant to citrus plants leaves diseases detection and the classification. The article presents a detailed taxonomy of citrus leaf diseases. Initially, the challenges of each step are discussed in detail, which affects the detection and classification accuracy. In addition, a thorough literature review of automated disease detection and classification methods is presented. To this end, we study different image preprocessing, segmentation, feature extraction, features selection, and classification methods. In addition, also discuss the importance of features extraction and deep learning methods. The survey presents the detailed discussion on studies, outlines their strengths and limitations, and uncovers further research issues. The survey results reveal that the adoption of automated detection and classification methods for citrus plants diseases is still in its infancy. Hence new tools are needed to fully automate the detection and classification processes.
Single cell protein (SCP) is a bulk of dried cells which can also termed as bioprotein, microbial protein or biomass. SCP is produced by the microorganisms such as algae, yeast, fungi and bacteria, ...however, fungi and bacteria are the major producers of this protein. High production of proteins from these sources was mainly due to their fast growth rate and relatively higher protein level in their chemical structure. Some algal species were also used for this purpose which specifically cultivated in the aquatic medium. In addition to high content of protein, SCP also contains carbohydrates, nucleic acids, fats, minerals and vitamins. Additionally, SCP has a high level of essential amino acids such as lysine, methionine, and threonine. This source of protein (SCP) has been proved a good replacement of other expensive protein sources like fish and soybean meals. Therefore, conclusion can be made that SCP can easily replace traditional (plant and animal) protein sources in human, animal as well as fish diets without any detrimental effect. Finally, in this review, we focus on new feeding trials on SCP in some aquaculture species, such as Atlantic salmon, white leg shrimp and rainbow trout.
•SCP is a bulk of dried cells which can also termed as bioprotein, microbial protein or biomass.•SCP is produced by the microorganisms such as algae, yeast, fungi and bacteria.•High production of proteins from SCP sources was mainly due to their fast growth rate.•SCP has a high level of essential amino acids such as lysine, methionine, and threonine.•SCP can prove to be a good replacement of other expensive protein sources like fish and soybean meals.
•Decorrelation formulation based contrast improvement.•Lesion segmentation using modified MASK RCNN.•Transfer Learning based CNN features are extracted.•A Entropy-controlled LS-SVM based best CNN ...features are selected.
Malignant melanoma is considered to be one of the deadliest types of skin cancers which is responsible for the massive number of deaths worldwide. According to the American Cancer Society (ACS), more than a million Americans are living with this melanoma. Since 2019, 192,310 new cases of melanoma are registered, where 95,380 are noninvasive, and 96,480 are invasive. The numbers of deaths due to melanoma in 2019 alone are 7,230, comprising 4,740 men and 2,490 women. Melanoma may be curable if diagnosed at the earlier stages; however, the manual diagnosis is time-consuming and also dependent on the expert dermatologist. In this work, a fully automated computerized aided diagnosis (CAD) system is proposed based on the deep learning framework. In the proposed scheme, the original dermoscopic images are initially pre-processed using the decorrelation formulation technique, which later passes the resultant images to the MASK-RCNN for the lesion segmentation. In this step, the MASK RCNN model is trained using the segmented RGB images generated from the ground truth images of ISBI2016 and ISIC2017 datasets. The resultant segmented images are later passed to the DenseNet deep model for feature extraction. Two different layers, average pool and fully connected, are used for feature extraction, which are later combined, and the resultant vector is forwarded to the feature selection block for down - sampling using proposed entropy-controlled least square SVM (LS-SVM). Three datasets are utilized for validation - ISBI2016, ISBI2017, and HAM10000 to achieve an accuracy of 96.3%, 94.8%, and 88.5% respectively. Further, the performance of MASK-RCNN is also validated on ISBI2016 and ISBI2017 to attain an accuracy of 93.6% and 92.7%. To further increase our confidence in the proposed framework, a fair comparison with other state-of-the-art is also provided.
Impinging jet heat transfer from curved surfaces is available in many industrial applications, such as cooling of gas turbine leading edge and deicing of airplane wing leading edge (heating). Most of ...the published studies on this topic involve non-swirling jet impingement from flat surfaces. Swirling jets are also used in some applications with the anticipation of heat transfer augmentation. Swirling impinging jet flow characteristics are significantly different from that of the non-swirling jet. However, fundamental studies of swirling jet impingement onto curved surfaces are very limited. As such, flow dynamics and convective heat transfer from a heated concave surface due to swirling impinging jets from a circular nozzle are numerically investigated using the RANS approach and the realizable k-ε turbulence model. Various flow and geometric parameters, such as the jet Reynolds number (Re) in the range of 11,600-35,000; the swirl number (Sw) in the range of 0-2.4; and the jet-to-surface separation distance (H) of 0.5-8 times the nozzle diameter, at a fixed impingement surface curvature (diameter) are studied. The results show that two counter-rotating recirculation zones appear near the target surface for near-field impingement (H≤1) and higher swirl numbers (Sw≥1.2), whereas for far-field impingements (H > 2), they are formed inside and outside of the main jet stream. An intense surface heat transfer zone develops only for near-field impingement in the range 1 ≤ S ≤ 3 at higher swirl numbers, where S is the distance along the impingement surface from the domain axis. More uniform thermal distribution along the curved surface is found for far-field impingement at Sw ≥ 0.8. Whilst the transitional Sw and H for Re = 11,600 are 0.8 and 2 respectively, the transitional Sw and H for Re = 35,000 are 0.8-1.2 and 4, respectively. Maximum heat transfer zones are found to be correlated with strong turbulence. Correlations are developed for the average Nusselt number as a function of the jet Reynolds number, jet exit to impingement surface separation distance, and swirling strength.
Brain tumor detection is an active area of research in brain image processing. In this work, a methodology is proposed to segment and classify the brain tumor using magnetic resonance images (MRI). ...Deep Neural Networks (DNN) based architecture is employed for tumor segmentation. In the proposed model, 07 layers are used for classification that consist of 03 convolutional, 03 ReLU and a softmax layer. First the input MR image is divided into multiple patches and then the center pixel value of each patch is supplied to the DNN. DNN assign labels according to center pixels and perform segmentation. Extensive experiments are performed using eight large scale benchmark datasets including BRATS 2012 (image dataset and synthetic dataset), 2013 (image dataset and synthetic dataset), 2014, 2015 and ISLES (Ischemic stroke lesion segmentation) 2015 and 2017. The results are validated on accuracy (ACC), sensitivity (SE), specificity (SP), Dice Similarity Coefficient (DSC), precision, false positive rate (FPR), true positive rate (TPR) and Jaccard similarity index (JSI) respectively.
•A new light-weight Deep Neural Networks approach for brain tumor segmentation.•Extensive evaluation of proposed model on eight challenging big datasets.•Proposed work achieves state-of-the-art accuracy on these benchmark datasets.•Comparison of presented work with sixteen existing techniques in the same domain.•Better results by proposed method without incurring heavy computational burden.
► Laminar mixed convection in a cavity with a hot square blockage inside is studied. ► The parameters are the blockage ratio, Reynolds number, and Richardson number. ► Various blockage sizes and ...concentric and eccentric placements are considered. ► With increasing blockage size average Nusselt number decrease initially. ► Nusselt numbers drop to a minima then increase with further blockage size increase.
Laminar mixed convection characteristics in a square cavity with an isothermally heated square blockage inside have been investigated numerically using the finite volume method of the ANSYS FLUENT commercial CFD code. Various different blockage sizes and concentric and eccentric placement of the blockage inside the cavity have been considered. The blockage is maintained at a hot temperature, Th, and four surfaces of the cavity (including the lid) are maintained at a cold temperature, Tc, under all circumstances. The physical problem is represented mathematically by sets of governing conservation equations of mass, momentum, and energy. The geometrical and flow parameters for the problem are the blockage ratio (B), the blockage placement eccentricities (ɛx and ɛy), the Reynolds number (Re), the Grashof number (Gr), and the Richardson number (Ri). The flow and heat transfer behavior in the cavity for a range of Richardson number (0.01–100) at a fixed Reynolds number (100) and Prandtl number (0.71) is examined comprehensively. The variations of the average and local Nusselt number at the blockage surface at various Richardson numbers for different blockage sizes and placement eccentricities are presented. From the analysis of the mixed convection process, it is found that for any size of the blockage placed anywhere in the cavity, the average Nusselt number does not change significantly with increasing Richardson number until it approaches the value of the order of 1 beyond which the average Nusselt number increases rapidly with the Richardson number. For the central placement of the blockage at any fixed Richardson number, the average Nusselt number decreases with increasing blockage ratio and reaches a minimum at around a blockage ratio of slightly larger than 1/2. For further increase of the blockage ratio, the average Nusselt number increases again and becomes independent of the Richardson number. The most preferable heat transfer (based on the average Nusselt number) is obtained when the blockage is placed around the top left and the bottom right corners of the cavity.
A general synthesis for 1,3-diphenylurea and 1,3-diphenylthiourea derivatives of mucolytic agent bromhexine is described by reactions of different commercially available phenyl isocyanates and ...phenyl-isothiocyanates at room temperature without any additional catalyst and additive. Using commercially available electron donating and electron withdrawing phenyl isocyanates in the transformation of bromhexine into urea derivatives proceeded from moderate to high product yield (42-90%). Bromhexine resulting compounds add a general interest in the fields of Pharmaceuticals, diagnostics and materials.
