Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images

Calcaneus fractures often occur because of accidents during exercise or activities. In general, the detection of the calcaneus fracture is still carried out manually through CT image observation, and as a result, there is a lack of precision in the analysis. This paper proposes a computer-aid method for the calcaneal fracture detection to acquire a faster and more detailed observation. First, the anatomical plane orientation of the tarsal bone in the input image is selected to determine the location of the calcaneus. Then, several fragments of the calcaneus image are detected and marked by color segmentation. The Sanders system is used to classify fractures in transverse and coronal images into four types, based on the number of fragments. In the sagittal image, fractures are classified into three types based on the involvement of the fracture area. The experimental results show that the proposed method achieves a high precision rate of 86%, with a fast computational performance of 133 frames per second (fps), used to analyze the severity of injury to the calcaneus. The results in the test image are validated based on the assessment and evaluation carried out by the physician on the reference datasets.

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Sparse-FCM and Deep Convolutional Neural Network for the segmentation and classification of acute lymphoblastic leukaemia

Biomedical Engineering / Biomedizinische Technik ◽

10.1515/bmt-2018-0213 ◽

2020 ◽

Vol 65 (6) ◽

pp. 759-773

Author(s):

Segu Praveena ◽

Sohan Pal Singh

Keyword(s):

Neural Network ◽

Acute Lymphoblastic Leukaemia ◽

Convolutional Neural Network ◽

Optimization Algorithm ◽

Lymphoblastic Leukaemia ◽

Input Image ◽

Deep Convolutional Neural Network ◽

Grey Wolf ◽

Deep Cnn

AbstractLeukaemia detection and diagnosis in advance is the trending topic in the medical applications for reducing the death toll of patients with acute lymphoblastic leukaemia (ALL). For the detection of ALL, it is essential to analyse the white blood cells (WBCs) for which the blood smear images are employed. This paper proposes a new technique for the segmentation and classification of the acute lymphoblastic leukaemia. The proposed method of automatic leukaemia detection is based on the Deep Convolutional Neural Network (Deep CNN) that is trained using an optimization algorithm, named Grey wolf-based Jaya Optimization Algorithm (GreyJOA), which is developed using the Grey Wolf Optimizer (GWO) and Jaya Optimization Algorithm (JOA) that improves the global convergence. Initially, the input image is applied to pre-processing and the segmentation is performed using the Sparse Fuzzy C-Means (Sparse FCM) clustering algorithm. Then, the features, such as Local Directional Patterns (LDP) and colour histogram-based features, are extracted from the segments of the pre-processed input image. Finally, the extracted features are applied to the Deep CNN for the classification. The experimentation evaluation of the method using the images of the ALL IDB2 database reveals that the proposed method acquired a maximal accuracy, sensitivity, and specificity of 0.9350, 0.9528, and 0.9389, respectively.

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CNN architecture comparison for radio galaxy classification

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab325 ◽

2021 ◽

Vol 503 (2) ◽

pp. 1828-1846

Author(s):

Burger Becker ◽

Mattia Vaccari ◽

Matthew Prescott ◽

Trienko Grobler

Keyword(s):

Comparative Study ◽

Galaxy Evolution ◽

Radio Galaxy ◽

Recognition Performance ◽

Model Complexity ◽

Great Promise ◽

Radio Sources ◽

Morphological Classification ◽

Computational Performance

ABSTRACT The morphological classification of radio sources is important to gain a full understanding of galaxy evolution processes and their relation with local environmental properties. Furthermore, the complex nature of the problem, its appeal for citizen scientists, and the large data rates generated by existing and upcoming radio telescopes combine to make the morphological classification of radio sources an ideal test case for the application of machine learning techniques. One approach that has shown great promise recently is convolutional neural networks (CNNs). Literature, however, lacks two major things when it comes to CNNs and radio galaxy morphological classification. First, a proper analysis of whether overfitting occurs when training CNNs to perform radio galaxy morphological classification using a small curated training set is needed. Secondly, a good comparative study regarding the practical applicability of the CNN architectures in literature is required. Both of these shortcomings are addressed in this paper. Multiple performance metrics are used for the latter comparative study, such as inference time, model complexity, computational complexity, and mean per class accuracy. As part of this study, we also investigate the effect that receptive field, stride length, and coverage have on recognition performance. For the sake of completeness, we also investigate the recognition performance gains that we can obtain by employing classification ensembles. A ranking system based upon recognition and computational performance is proposed. MCRGNet, Radio Galaxy Zoo, and ConvXpress (novel classifier) are the architectures that best balance computational requirements with recognition performance.

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Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

10.21203/rs.3.rs-66836/v2 ◽

2021 ◽

Author(s):

Ali Mohammad Alqudah ◽

Shoroq Qazan ◽

Ihssan S. Masad

Keyword(s):

Artificial Intelligence ◽

Input Image ◽

Image Size ◽

X Ray ◽

Softmax Classifier ◽

Non Invasive ◽

Efficient Detection ◽

X Ray Imaging ◽

Chest X Ray

Abstract BackgroundChest diseases are serious health problems that threaten the lives of people. The early and accurate diagnosis of such diseases is very crucial in the success of their treatment and cure. Pneumonia is one of the most widely occurred chest diseases responsible for a high percentage of deaths especially among children. So, detection and classification of pneumonia using the non-invasive chest x-ray imaging would have a great advantage of reducing the mortality rates.ResultsThe results showed that the best input image size in this framework was 64 64 based on comparison between different sizes. Using CNN as a deep features extractor and utilizing the 10-fold methodology the propose artificial intelligence framework achieved an accuracy of 94% for SVM and 93.9% for KNN, a sensitivity of 93.33% for SVM and 93.19% for KNN and a specificity of 96.68% for SVM and 96.60% for KNN.ConclusionsIn this study, an artificial intelligence framework has been proposed for the detection and classification of pneumonia based on chest x-ray imaging with different sizes of input images. The proposed methodology used CNN for features extraction that were fed to two different types of classifiers, namely, SVM and KNN; in addition to the SoftMax classifier which is the default CNN classifier. The proposed CNN has been trained, validated, and tested using a large dataset of chest x-ray images contains in total 5852 images.

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Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset

Sensors ◽

10.3390/s21175813 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5813

Author(s):

Muhammad Umair ◽

Muhammad Shahbaz Khan ◽

Fawad Ahmed ◽

Fatmah Baothman ◽

Fehaid Alqahtani ◽

...

Keyword(s):

Transfer Learning ◽

Input Image ◽

Fine Tuning ◽

Feature Maps ◽

Virus Family ◽

X Ray ◽

Normal Chest ◽

Chest X Ray ◽

Confusion Matrices

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

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Prevalence of Peroneal Tendon Instability in Calcaneus Fractures

Foot & Ankle Orthopaedics ◽

10.1177/2473011418s00140 ◽

2018 ◽

Vol 3 (3) ◽

pp. 2473011418S0014

Author(s):

Abduljabbar Alhammoud ◽

Karim Mahmoud Khamis

Keyword(s):

English Language ◽

Lateral Wall ◽

Calcaneal Fracture ◽

Peroneal Tendon ◽

Foot And Ankle ◽

Calcaneal Fractures ◽

Distal Fibula ◽

Calcaneus Fracture ◽

Orthopedic Surgeons ◽

Calcaneus Fractures

Category: Trauma Introduction/Purpose: Peroneal tendon instability (PI) associated with intraarticular calcaneal fractures is a significant and commonly missed injury. It has been proposed that the peroneal tendon dislocates with the occurrence of a calcaneal fracture because the axial force transmitted blows out the lateral wall causing either impingement of the peroneal tendon or avulsion of the superficial peroneal retinaculum from the distal fibula, allowing the peroneal tendons to dislocate anteriorly. To date, peroneal tendon instability with a calcaneal fracture has often passed undiagnosed by both radiologists and orthopedic surgeons. The present review aimed to determine the global reported prevalence of peroneal instability associated with calcaneus fractures according to the severity of the calcaneus fracture classification and the significance of a fleck sign as an indicator of PI. Methods: An electronic search of MEDLINE, Scopus, and Google Scholar was conducted until June 2017 without a date restriction and for English-language reports only in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Database searching was augmented by manual searches of common journals in the field (JBS, Foot and Ankle International, Foot and Ankle Surgery, Journal of Orthopedic Trauma). The reference lists from the identified studies were also scrutinized for any additional reports.The studies that reported the prevalence/incidence of peroneal tendon subluxation/ dislocation in association with a calcaneus fracture were included whereas patients with calcaneus fractures combined with other fractures, extraarticular calcaneus fractures, and old calcaneus fractures were excluded.The data analysis was performed using a comprehensive meta-analysis with a random effects model. Results: Of 31 initial studies, 9 were included, contain 1027 patients/1050 calcaneus fractures.Most of the patients were male, with an average age of 42.03 (range 18 to 79) years.Of the 1050 calcaneus fractures, PI was found in association with 305.The pooled odds ratio of the prevalence of PI with calcaneus fractures was 29.3%(95% CI 25.9%-32.9%; p < .001).The prevalence of PI increased with an increased fracture severity using the Sanders classification, with a prevalence of 5.4% with Sanders I,19% with Sanders II, 39.4% in Sanders III, and 49.5% with Sanders IV. The prevalence of PI in the tongue type fracture using the Essex-Lopresti classification was 19%, and for joint depression, was 30.8% (p < .001). The prevalence of the fleck sign as an indicator of PI was 54.7%. Conclusion: The global reported prevalence of peroneal tendon dislocation associated with intraarticular calcaneal fractures was 29.3% and that the prevalence of PI increases with increased severity of the calcaneus fracture. Such a high prevalence should raise suspicions in both radiologists and orthopedic surgeons about this injury. The assessment should begin with a careful review of preoperative radiographs to search for the fleck sign and CT scans for a dislocated tendon, followed later by intraoperative assessment of superior peroneal retinaculum integrity.

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Novel MILP Models for Scheduling Permutation Flowshops

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1176 ◽

2008 ◽

Vol 3 (1) ◽

Cited By ~ 2

Author(s):

Suresh S Pitty ◽

Iftekhar A Karimi

Keyword(s):

Model Building ◽

Evaluation Methodology ◽

Mixed Integer ◽

Test Problems ◽

Small Problem ◽

Numerical Studies ◽

Computational Performance ◽

Intermediate Storage ◽

Scheduling Models

Flowshop scheduling via mixed integer linear programming (MILP) has received considerable attention in the past four decades. However, alternate models are limited; most numerical studies have used small problem sizes. A need for good model evaluation methodology exists; and limited work exists on flowshops with no intermediate storage. This paper presents a classification of flowshops and MILP scheduling models, and addresses some of these issues. It develops a host of new MILP formulations for minimizing makespan in a permutation flowshop with no storage and with or without unit setups. It presents some useful insights into model building by employing a variety of new and old binary variables and coupling them creatively. In contrast to previous work, it evaluates a range of new and existing MILP models using many larger test problems with no or unlimited intermediate storage, and presents a reliable procedure to rank various models based on problems with varying data and sizes. It shows that the top models for the two flowshops indeed show slightly different computational performance.

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The classification of skateboarding tricks via transfer learning pipelines

PeerJ Computer Science ◽

10.7717/peerj-cs.680 ◽

2021 ◽

Vol 7 ◽

pp. e680

Author(s):

Muhammad Amirul Abdullah ◽

Muhammad Ar Rahim Ibrahim ◽

Muhammad Nur Aiman Shapiee ◽

Muhammad Aizzat Zakaria ◽

Mohd Azraai Mohd Razman ◽

...

Keyword(s):

Transfer Learning ◽

Input Image ◽

Computational Time ◽

Support Vector ◽

Svm Classifier ◽

Test Accuracy ◽

Learning Models ◽

Test Dataset ◽

Flat Ground

This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with grid-searched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWT-MobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks execution.

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Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

10.21203/rs.3.rs-66836/v1 ◽

2020 ◽

Author(s):

Ali Mohammad Alqudah ◽

Shoroq Qazan ◽

Ihssan S. Masad

Keyword(s):

Artificial Intelligence ◽

Input Image ◽

Image Size ◽

X Ray ◽

Softmax Classifier ◽

Non Invasive ◽

Efficient Detection ◽

X Ray Imaging ◽

Chest X Ray

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Lemon Leaf Disease Detection and Classification using SVM and CNN

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.d4245.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 11485-11488

Keyword(s):

Image Processing ◽

Plant Disease ◽

Color Space ◽

Input Image ◽

Plant Diseases ◽

Image Processing Technique ◽

Support Vector ◽

Training Time ◽

Leaf Disease

India is a developing country and agriculture has always played a major role in bolstering the country’s economic growth. Due to various factors like industrialization, mechanization and globalization, the green fields are facing complications. So, identifying the plant disease incorrectly will lead to a huge loss of both quantity and quality of the product and it will also incur loss in time and money. Hence, identifying the condition of the plant plays a major role for successful cultivation. Now a day’s image processing technique is being employed as a focal technique for diagnosing the various features of the crop. The image processing techniques can be used for identification of the plant disease and hence classify the plant disease. Generally, the symptoms of the disease are observed on leaves, stems, flowers etc. Here, the leaves of the affected plant are used for the identification and classification of the disease. Leaf image is captured using a smart phone as the first step and then they are processed to determine the condition of the plant. Identification of plant disease follows the steps like loading the image of the plant leaf, histogram equalization for enhancing contrast of the image, segmentation process by using Lab color space model, extracting features of the segmented image using GLCM (Grey Level Cooccurrence Matrix) and finally classification of leaf disease by using MCSVM (Multi Class Support Vector Machine).This procedure obtained an accuracy percentage of 83.6%.Also, it takes long training time for large datasets. To improve the accuracy of the detection and the classification of the plants, Convolutional Neural Network (CNN) is used. The main advantage of CNN is that it automatically detects the main features of the input without any supervision of human. In CNN identification of disease follow the steps like loading the image as the input image, convolution of the feature map and finally max pooling the layers to calculate the features of the image in detail. The plant diseases are classified with an accuracy of 93.8 %.

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Vacant Parking Slot Detection in the Around View Image Based on Deep Learning

Sensors ◽

10.3390/s20072138 ◽

2020 ◽

Vol 20 (7) ◽

pp. 2138 ◽

Cited By ~ 2

Author(s):

Wei Li ◽

Libo Cao ◽

Lingbo Yan ◽

Chaohui Li ◽

Xiexing Feng ◽

...

Keyword(s):

Deep Learning ◽

Detection Method ◽

Recall Rate ◽

Visual Environment ◽

Convolution Kernel ◽

Number Of Layers ◽

Detection Stage ◽

Geometric Cues ◽

Precision Rate

Due to the complex visual environment, such as lighting variations, shadows, and limitations of vision, the accuracy of vacant parking slot detection for the park assist system (PAS) with a standalone around view monitor (AVM) needs to be improved. To address this problem, we propose a vacant parking slot detection method based on deep learning, namely VPS-Net. VPS-Net converts the vacant parking slot detection into a two-step problem, including parking slot detection and occupancy classification. In the parking slot detection stage, we propose a parking slot detection method based on YOLOv3, which combines the classification of the parking slot with the localization of marking points so that various parking slots can be directly inferred using geometric cues. In the occupancy classification stage, we design a customized network whose size of convolution kernel and number of layers are adjusted according to the characteristics of the parking slot. Experiments show that VPS-Net can detect various vacant parking slots with a precision rate of 99.63% and a recall rate of 99.31% in the ps2.0 dataset, and has a satisfying generalizability in the PSV dataset. By introducing a multi-object detection network and a classification network, VPS-Net can detect various vacant parking slots robustly.

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