Statistical Texture Features Research Articles

7847 Radiomic Ultrasound Checkup of Thyroid Nodules Before Radiodiodine Therapy

Abstract Disclosure: A.A. Trukhin: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). A.V. Manaev: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). S.M. Zakharova: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). M.S. Sheremeta: None. E.A. Troshina: Grant Recipient; Self; Russian Science Foundation (project N 22-15-00135). Background: Over 83 years radioiodine therapy (RIT) of autonomous thyroid nodules still have uncovered pages, according to its complexity and thyroid nodules variety. There are two main criteria to allow RIT – no malignancy and presence of iodine uptake, the last one could be checked by Tc-99m-pertachnetate or I-123, I-131 iodine scintigraphy. Case with thyroid nodules first undergo ultrasound (US) TI-RADS classification, then fine needle aspiration stratification according to the Bethesda system. Publications applied such algorithm shows 75-85% accuracy in malignancy determination, that in 15-25% can lead to improper RIT application. Aim: Method development for thyroid nodules ultrasound checkup before I-131 administration and I-131 24h uptake prognosis. Methods: Study includes 150 nodules (65 benign, 60 malignant, 25 autonomous benign thyroid nodules). Both US longitudinal and transversal thyroid nodule projections were obtained via GE Voluson E8 (36% benign cases / 27% malignant cases) and GE Logiq E (64% benign cases / 73% malignant cases) in cinematic loop regime. Autonomous thyroid nodules underwent I-131 pharmacokinetics examination with 24h uptake determination and RIT. Statistical textural features were obtained through spatial adjacency matrix. Results: Analysis of 16 statistical textural features shows weak correlation between 11 statistical textural features E (Energy), S (Entropy), LU (Local homogeneity), MP (Maximum probability), TR (Matrix trace), CORR (Correlation), IMC (information measure of correlation), DV (Difference variance), DE (Difference entropy), SE (Sum entropy), SA (Average value of the sum). ROC-analysis using 11 statistical textural features shows the best model for benign/malignance determination with AUC = 0.83, Se = 74%, Sp = 73%, PPV = 75%, NPV = 72%, Acc = 74%. I-131 pharmacokinetics examination shows strong correlation SA to I-131 24h uptake. Conclusions: The performed study demonstrates the possibility of using textural statistical features of ultrasound images as instrument for thyroid nodules ultrasound checkup before I-131 administration, for extra FNA prescription and prognosis the I-131 24h important in exact therapeutic I-131 activity calculation. Presentation: 6/2/2024

A Novel Technique for Facial Recognition Based on the GSO-CNN Deep Learning Algorithm

Face recognition is one of the important elements that can be used for securing the facilities, emotion recognition, sentiment exploration, fraud analysis, and traffic pattern analysis. Intelligent face recognition has yielded excellent accuracy in a controlled environment whereas vice versa in an uncontrolled environment. However, conventional methods can no longer satisfy the demand at present due to their low recognition accuracy and restrictions on many occasions. This study proposed an optimal deep learning-based face recognition system that improves the security of the model developed in the IoT cloud environment. Initially, the dataset of images was gathered from the public repository. The captured images are explored using image processing techniques like image preprocessing employing the Gaussian filter technique for removing the noise and smoothing the image. The histogram of oriented gradients (HOGs) is used for the image segmentation. The processed images are preserved at the cloud service layer. Extract features were linked to facial activities using the spatial-temporal interest point (STIP). On the other hand, the extracted feature vectors are investigated using galactic swarm optimization (GSO) techniques that give optimized feature vectors. The necessary features are selected using the gray level co-occurrence matrix (GLCM), which separates the statistical texture features. The GSO output is fed into the deep convolutional neural network (DCNN) that effectively trains the captured face images. This will allow the effectiveness of the GSO-CNN technique to be assessed in terms of recognition accuracy, recall, precision, and error rate.

Acute lymphocytic leukemia detection and subtype classification via extended wavelet pooling based-CNNs and statistical-texture features

Acute lymphoblastic leukemia (ALL) is considered the most fatal form of leukemia (also known as blood cancer). It propagates quickly among adults and children and could lead to their death. Early detection of ALL and ALL subtypes is the key factor in selecting effective treatment types and improving survival rates. However, routine diagnostic approaches have several drawbacks. Computer-assisted diagnosis (CAD) is the perfect solution to avoid these challenges and achieve a fast and accurate diagnosis. Current CAD models require enhancement/segmentation processing. Besides, they are either dependent on deep learning (DL) models or handcrafted features along with machine learning. Those CADs that employed DL approaches relied solely on spatial information during the training procedure. However, learning them with spectral temporal and temporal representations could improve performance. Furthermore, integrating deep features from DL models along with handcrafted features can increase the discrimination ability of attributes in medical image classification. This study aims to propose a novel CAD for ALL detection and subtype classification without pre-segmentation or enhancement steps. The proposed CAD extends the conventional DL models of convolutional neural networks by introducing an additional wavelet pooling, accompanied by a dense layer or a long-short-term memory (LSTM) layer, and then a SoftMax layer, acquiring spectral-temporal information along with temporal information. To further improve the framework's ability to discriminate, the introduced CAD then combines the wavelet-based deep features of every CNN with numerous handcrafted attributes. Afterward, a feature selection methodology is utilized to create a model with limited features and improved accuracy. The performance results show that the novel CAD is capable of achieving 100% ALL detection accuracy, as well as 100% ALL-subtype classification accuracy with just 88 and 146 features. Thus, this CAD can be employed to assist pathologists in the rapid and precise ALL identification and subcategories recognition.

STFE-Net: A multi-stage approach to enhance statistical texture feature for defect detection on metal surfaces

Statistical texture features are essential for metal surface defect detection. However, low contrast and cluttered backgrounds exacerbate the intrinsic blurriness and variability of defect textures, hampering accurate identification and localization. To tackle these challenges, we propose a Statistical Texture Feature Enhancement Network (STFE-Net) based on YOLO model, a multi-stage approach based on pooling attention mechanism and cross-scale shallow feature reinforcement. Firstly, we enhance the image contrast and propose a Stacked Pooling Attention Residual (SPA-Res) module to capture better texture features; then, we embed the Shallow Feature Weighted (SFW) branches to achieve cross-layer shallow feature reinforcement and add feature layer (AFL) to extract rich semantic features; and finally, we propose an Efficient Complete Intersection over Union (ECIoU) loss function and employ four detection heads to achieve precise localization of defects. On the NEU-DET dataset, our model achieves the state-of-the-art performance. Especially, the mean Average Precision (mAP@IoU = 0.5) of our model is 82.3 %, which is 6.2 % higher than baseline network. With the input resolution of 640 × 640, STFE-Net achieves 54 FPS inference speed with a GeForce GTX 1080. In addition, the experimental results on the DAGM dataset and GC10-DET dataset demonstrate that the proposed model has excellent generalization ability.

A novel method for vegetable and fruit classification based on using diffusion maps and machine learning

Vegetable and fruit classification can help all links of agricultural product circulation to better carry out inventory management, logistics planning and supply chain coordination, and improve the efficiency and response speed of the supply chain. However, the current classification of vegetables and fruits mainly relies on manual classification, which inevitably introduces the influence of human subjective factors, resulting in errors and misjudgments in the classification of vegetables and fruits. In response to this serious problem, this research proposes an efficient and reproducible novel model to classify multiple vegetables and fruits using handcrafted features. In the proposed model, preprocessing operations such as Gaussian filtering, grayscale and binarization are performed on the pictures of vegetables and fruits to improve the quality of the pictures; statistical texture features representing vegetable and fruit categories, wavelet transform features and shape features are extracted from the preprocessed images; the feature dimension reduction method of diffusion maps is used to reduce the redundant information of the combined features composed of statistical texture features, wavelet transform features and shape features; five effective machine learning methods were used to classify the types of vegetables and fruits. In this research, the proposed method was rigorously verified experimentally and the results show that the SVM classifier achieves 96.25% classification accuracy of vegetables and fruits, which proves that the proposed method is helpful to improve the quality and management level of vegetables and fruits, and provide strong support for agricultural production and supply chain.

Hepatocellular Carcinoma Diagnosis Based on Ultrasound Images Using Feature Selection Techniques and K-nearest Neighbor Classifier

Background: Liver cancer is one of the most common types of cancer, in which early detection plays a significant role in preventing progression and reducing mortality. Ultrasound is one of the methods of liver examination recommended by guidelines due to its performance in detecting focal liver lesions. These small lesions may be missed in the early stages or diagnosed only when the prognosis is poor. Objectives: This study aimed to implement the best classification model for two liver stages by extracting optimal feature subsets to be used in computer-aided diagnosis systems (CAD). Methods: The model classifies the liver into two stages using B-mode ultrasound images of the liver. It involves extracting statistical texture features utilizing discrete wavelet transform (DWT) and gray level co-occurrence matrix (GLCM). This study applied two feature selection methods: t-test and sequential forward floating selection (SFFS). The subset of selected features was presented to the k-nearest neighbor classifier for incorporation into a CAD system. Results: The accuracy, sensitivity, and specificity of the k-NN classifier were 98.75%, 98.82%, and 99.1%, respectively. Conclusions: Image analysis approaches were successfully performed to extract and select useful features. Therefore, this model is recommended for classifying two liver stages, normal and HCC.

Autonomous Multi-modality Burn Wound Characterization using Artificial Intelligence.

Between 5% and 20% of all combat-related casualties are attributed to burn wounds. A decrease in the mortality rate of burns by about 36% can be achieved with early treatment, but this is contingent upon accurate characterization of the burn. Precise burn injury classification is recognized as a crucial aspect of the medical artificial intelligence (AI) field. An autonomous AI system designed to analyze multiple characteristics of burns using modalities including ultrasound and RGB images is described. A two-part dataset is created for the training and validation of the AI: in vivo B-mode ultrasound scans collected from porcine subjects (10,085 frames), and RGB images manually collected from web sources (338 images). The framework in use leverages an explanation system to corroborate and integrate burn expert's knowledge, suggesting new features and ensuring the validity of the model. Through the utilization of this framework, it is discovered that B-mode ultrasound classifiers can be enhanced by supplying textural features. More specifically, it is confirmed that statistical texture features extracted from ultrasound frames can increase the accuracy of the burn depth classifier. The system, with all included features selected using explainable AI, is capable of classifying burn depth with accuracy and F1 average above 80%. Additionally, the segmentation module has been found capable of segmenting with a mean global accuracy greater than 84%, and a mean intersection-over-union score over 0.74. This work demonstrates the feasibility of accurate and automated burn characterization for AI and indicates that these systems can be improved with additional features when a human expert is combined with explainable AI. This is demonstrated on real data (human for segmentation and porcine for depth classification) and establishes the groundwork for further deep-learning thrusts in the area of burn analysis.

Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques

Apparent diffusion coefficient (ADC) of magnetic resonance imaging (MRI) is an indispensable imaging technique in clinical neuroimaging that quantitatively assesses the diffusivity of water molecules within tissues using diffusion-weighted imaging (DWI). This study focuses on developing a robust machine learning (ML) model to predict the aggressiveness of gliomas according to World Health Organization (WHO) grading by analyzing patients’ demographics, higher-order moments, and grey level co-occurrence matrix (GLCM) texture features of ADC. A population of 722 labeled MRI-ADC brain image slices from 88 human subjects was selected, where gliomas are labeled as glioblastoma multiforme (WHO-IV), high-grade glioma (WHO-III), and low-grade glioma (WHO I-II). Images were acquired using 3T-MR systems and a region of interest (ROI) was delineated manually over tumor areas. Skewness, kurtosis, and statistical texture features of GLCM (mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence, and shade) were calculated using ADC values within ROI. The ANOVA f-test was utilized to select the best features to train an ML model. The data set was split into training (70%) and testing (30%) sets. The train set was fed into several ML algorithms and selected most promising ML algorithm using K-fold cross-validation. The hyper-parameters of the selected algorithm were optimized using random grid search technique. Finally, the performance of the developed model was assessed by calculating accuracy, precision, recall, and F1 values reported for the test set. According to the ANOVA f-test, three attributes; patient gender (1.48), GLCM energy (9.48), and correlation (13.86) that performed minimum scores were excluded from the dataset. Among the tested algorithms, the random forest classifier(0.8772 ± 0.0237) performed the highest mean-cross-validation score and selected to build the ML model which was able to predict tumor categories with an accuracy of 88.14% over the test set. The study concludes that the developed ML model using the above features except for patient gender, GLCM energy, and correlation, has high prediction accuracy in glioma grading. Therefore, the outcomes of this study enable to development of advanced tumor classification applications that assist in the decision-making process in a real-time clinical environment.

Texture Characterization Fuzzy Logic-Based Model for Melanoma Diagnosis

Over the past two decades, the world has known a significant number of deaths from cancer. More specifically, melanoma which is considered the deadliest form of skin cancer causes a remarkable percentage of all cancer deaths. Therefore, the health and disease management community has exceedingly invested in creating efficient devices that help dermatologists to early evaluate and inspect a specific kind of skin disease. These systems use some measurable visual component describing the shape, color, and texture of skin diseases to recognize them and to specify their malignancy. In this article, a fuzzy logic-based model is introduced which characterizes statistical texture features. The fuzzy model is built to classify skin lesions into malignant and benign based on the characterization of statistical texture features. This method has been proven to yield reliable results compared to the machine learning-based model. Besides, it is more stable and unbiased in classifying skin lesions than machine learning models.

Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning

As the world continues to battle the devastating effects of the COVID-19 pandemic, it has become increasingly crucial to screen patients for contamination accurately and effectively. One of the primary screening methods is chest radiography, utilizing radiological imaging to detect the presence of the virus in the lungs. This study presents a cutting-edge solution to classify COVID-19 infections in chest X-ray images by utilizing the Gray-Level Co-occurrence Matrix (GLCM) and machine learning algorithms. The proposed method analyzes each X-ray image using the GLCM to extract 22 statistical texture features and then trains two machine learning classifiers - K-Nearest Neighbor and Support Vector Machine - on these features. The method was tested on the COVID-19 Radiography Database and was compared to a state-of-the-art method, delivering highly efficient results with impressive sensitivity, accuracy, precision, F1-score, specificity, and Matthew's correlation coefficient. The proposed approach offers a promising new way to classify COVID-19 infections in chest X-ray images and has the potential to play a crucial role in the ongoing fight against the pandemic.

Classification of brain tumour based on texture and deep features of magnetic resonance images

AbstractAccording to the world health organization report, brain cancer has the highest death rate. magnetic resonance imaging (MRI) for detecting brain tumours is adopted these days due to several advantages over other detection techniques. This paper presents a novel methodology to classify MR images based on texture and deep features, z‐score normalization, and, Comprehensive learning elephant herding optimization (CLEHO) based feature optimization and classification. Deep features of brain MR images have been extracted through DenseNet121 convolutional neural network and texture features have been extracted by using the Gabor 2D filter, Haralick texture feature, edge continuity texture feature, first order statistical texture feature, local binary pattern feature, difference theoretic texture feature, and spectral texture feature techniques. Normalization has been done using three normalization techniques that is, z score, mean median absolute deviation (MMAD), and Tanh‐based after aggregating the features extracted from the previous step. z‐score normalization has been suggested for feature normalization after comparing the results attained from the three techniques. Lastly, binary CLEHO has been proposed for selecting an optimal feature set and also optimizing the ‘k’ value of the k‐NN classifier. The outcome of this proposed work is compared with other state‐of‐the‐art methods for a publicly available magnetic resonance image Fighshare dataset of 3064 slices from 233 patients. The proposed work has a brain tumour average classification accuracy of 98.97%, which is better than the other state‐of‐the‐art methods. The proposed work can be used to assist the radiologist in the screening of multi‐class brain tumours.

PolSAR Image Building Extraction with G0 Statistical Texture Using Convolutional Neural Network and Superpixel

Polarimetric synthetic aperture radar (PolSAR) has unique advantages in building extraction due to its sensitivity to building structures and all-time/all-weather imaging capabilities. However, the structure of buildings is complex, and buildings are easily confused with other objects in polarimetric SAR images. The speckle noise of SAR images will affect the accuracy of building extraction. This paper proposes a novel building extraction approach from PolSAR images with statistical texture and polarization features by using a convolutional neural network and superpixel. A feature space that is sensitive to building, including G0 statistical texture and PualiRGB features, is constructed and used as CNN input. Considering that the building boundary of the CNN classification result is inaccurate due to speckle noise, the simple linear iterative cluster (SLIC) superpixel is utilized to constrain the building extraction result. Finally, the effectiveness of the proposed method has been verified by experimenting with PolSAR images from three different sensors, including ESAR, GF-3, and RADARSAT-2. Experiment results show that compared with the other five PolSAR building extraction methods including threshold, SVM, RVCNN, and PFDCNN, our method without superpixel constraint, the F1-score of this method is the highest, reaching 84.22%, 91.24%, and 87.49%, respectively. The false alarm rate of this method is at least 10% lower and the F1 index is at least 6% higher when the building extraction accuracy is comparable. Further, the discussion and method parameter analysis results show that increasing the use of G0 statistical texture parameters can improve building extraction accuracy and reduce false alarms, and the introduction of superpixel constraints can further reduce false alarms.

Development of welding discontinuity identification system using statistical texture feature extraction and ANN classification on digital radiographic image

Discontinuity in welds is one of the causes of the quality of a connection in the material decreases function. Undamaged test with radiographic method is one of the tests to see the quality of a weld. The test results are radiograph images and evaluated by a radiographer. So this research is designed by optimizing a system to help the work of a radiography expert in identifying discontinuities by utilizing the Matlab Application. On this system uses the method of characteristic extraction and classification of neural networks (AAN). The system uses a characteristic extraction method with geometric invariant moment (GIM) algorithms and a gray level co-occurenece matrix (GLCM) as identification values used in the classification process. The calcification process uses a backpropagation-type multilayer Artificial Neural Network. The types of discontinuities used as data in this system are incompleted of penetration, crack, wormhole, and distributed porosity using a total of 800 datasets of radiograph imagery data. This data sharing is organized using k fold cross validation. The study conducted 15 experiments in system testing to prove the truth in identifying. The results of the experiment resulted in the highest average performance score reaching 93.33%

Analysis on significance of various statistical texture features in vision-based surface roughness prediction in end milling process

Analysis on significance of various statistical texture features in vision-based surface roughness prediction in end milling process

COVID-19 CT-images diagnosis and severity assessment using machine learning algorithm.

As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage.

Fault diagnosis for overcharge and undercharge conditions in refrigeration systems using infrared thermal images

With the increase in industrialization and the number of people and buildings, the need for refrigeration systems has also increased. Maintenance of these systems, malfunctions and their late detection cause time and cost problems. Therefore, in this study, a machine learning application is recommended to diagnose the refrigerant undercharge and refrigerant overcharge faults that may occur in the refrigeration system by using infrared images. Firstly, infrared images obtained from normal charge, undercharge and overcharge situations in the refrigeration system, are passed through the two-dimensional discrete wavelet transform (2D-DWT) and the images are decomposed. Then, statistical texture features from the original input images are obtained by separating infrared images. The dimensionality of the extracted features is reduced using the principal component analysis (PCA) and the ReliefF (RF) algorithm. Finally, these selected features are applied to the K nearest neighbor (k-NN), naive Bayes algorithm (NBA), decision tree (DT), and cascade forward neural network (CFNN) classifiers. It has been found that RF-based feature selection is useful in obtaining the optimal feature vector. The classification results revealed that CFNN outperforms k-NN, NBA, and DT. Compared to traditional electrical measurements and fault detection methods, it has been shown that the recommended system is feasible due to its features such as ease of use, remote measurement, and self-adaptive recognition.

Statistical Texture Learning Method for Monitoring Abandoned Suburban Cropland Based on High-Resolution Remote Sensing and Deep Learning

Cropland abandonment is crucial in agricultural management and has a profound impact on crop yield and food security. In recent years, many cropland abandonment identification methods based on remote sensing observation data have been proposed, but most of these methods are based on coarse-resolution images and use traditional machine learning methods for simple identification. To this end, we perform abandonment recognition on high-resolution remote sensing images. According to the texture features of the abandoned land, we combine the method of statistical texture learning and propose a new deep learning framework called PSPNet-STL. The model integrates high-level semantic feature extraction and deep mining of low-level texture features to identify cropland abandonment. First, we labeled the abandoned cropland area and built the HRAC dataset, a high-resolution cropland abandonment dataset. Secondly, we improved PSPNet by fusing statistical texture learning modules to learn multiple texture information on low-level feature maps, and combined high-level semantic features for cropland abandonment recognition. Experiments are performed on the HRAC dataset. Compared with other methods, the proposed model has the best performance on this dataset, both in terms of accuracy and visualization, proving that deep mining of low-level statistical texture features is beneficial for crop abandonment recognition.

An Enhanced Diabetic Foot Ulcer Classification Approach Using GLCM and Deep Convolution Neural Network

Diabetic Foot Ulcers (DFU) are considered to be a common complication of diabetes, usually resulting in the amputation of lower extremities. Therefore, diagnosing this disease at an early stage is necessary to avoid the accompanying treatment approach, and this results in a significant cost reduction for the patient. To achieve an early diagnosis of this disease, we need to classify a patient's skin as normal or abnormal. A classification process relies heavily on the extracted features. So, we proposed a new technique called CNN_GLCMNet for feature extraction. This technique relies on Convolution Neural Network (CNN) and the Gray-Level Co-Occurrence Matrix (GLCM) techniques to mine abstract features and second-order statistical texture features. Also, Singular Value Decomposition (SVD) is applied to reduce the dimensionality of the obtained features that result from CNN, Next, the GLCM method is applied to extract second-order statistical texture features. Then, these two kinds of features (abstract features and statistical features) are combined and used as input for the classifier. Two classification mechanisms have been adopted in the classification of images into normal and abnormal skin. First, the Deep Neural Network (DNN) classifier achieves the following performance evaluation metrics (accuracy 97.43%, recall 97.25%, specificity 97.59%, precision 97.53%, f1-score 97.38%). Second, the Support Vector Machine (SVM) classifier achieves the following performance evaluation metrics (accuracy 96.93%, recall 96.99%, specificity 96.94%, precision 96.76%, f1-score 96.85%). Since both classifiers have been validated against the DFU dataset using 10-fold cross-validation. The DNN classifiers with our new feature extraction technique achieve better results in terms of accuracy, specificity, precision, recall, and f1-score than in previous work. Furthermore, a comparison of DNN and SVM classifiers finds that DNN gives a better result according to performance metrics.

Machine Vision Approach for Diagnosing Tuberculosis (TB) Based on Computerized Tomography (CT) Scan Images

Tuberculosis is curable, still the world’s second inflectional murderous disease, and ranked 13th (in 2020) by the World Health Organization on the list of leading death causes. One of the reasons for its fatality is the unavailability of modern technology and human experts for early detection. This study represents a precise and reliable machine vision-based approach for Tuberculosis detection in the lung through Symmetry CT scan images. TB spreads irregularly, which means it might not affect both lungs equally, and it might affect only some part of the lung. That’s why regions of interest (ROI’s) from TB infected and normal CT scan images of lungs were selected after pre-processing i.e., selection/cropping, grayscale image conversion, and filtration, Statistical texture features were extracted, and 30 optimized features using F (Fisher) + PA (probability of error + average correlation) + MI (mutual information) were selected for final optimization and only 6 most optimized features were selected. Several supervised learning classifiers were used to classify between normal and infected TB images. Artificial Neural Network (ANN: n class) based classifier Multi-Layer Perceptron (MLP) showed comparatively better and probably best accuracy of 99% with execution time of less than a second, followed by Random Forest 98.83%, J48 98.67%, Log it Boost 98%, AdaBoostM1 97.16% and Bayes Net 96.83%.

Automatic liver tumor segmentation and identification using fully connected convolutional neural network from CT images

SummaryIn recent years, one of the largest causes of death in human beings is liver tumor and cancer. In the current scenario, identifying the cancer tumor manually is very difficult and takes a lot of time as the world is battling with COVID‐19. The doctors and physicians are busy in serving and curing them. To predict the stage of liver tumor and plan the treatment, the segmentation is done from CT scanned images. In this research, two‐stage automatic liver segmentation and tumor identification framework using a fully connected convolutional neural network (FC‐CNN) model is proposed. During the first stage, the liver region is segmented using level set method from the preprocessed (OTSU thresholding) CT images. The features are extracted and utilized to detect the liver tumor in the second stage. The developed FC‐CNN is trained and tested using the extracted second order statistical textural features to classify the tumor affected and normal image. The proposed FC‐CNN model is trained and tested with 3D‐IRCADb‐01 and Kaggle datasets. The tested results prove that the proposed FC‐CNN model outperforms other reported methods. From the performance analysis, it is observed that it achieves a good accuracy and sensitivity rate of 99.11% and 98.10%, respectively.