Input X-ray Image Research Articles

Ensemble CNN Model for Computer-Aided Knee Osteoarthritis Diagnosis

Osteoarthritis (OA) is a multifaceted ailment posing challenges in its diagnosis and treatment due to the intricate nature of the disease. Particularly, Knee Osteoarthritis (KOA) significantly impacts the knee joint, manifesting through symptoms such as pain, stiffness, and limited movement. Despite its prevalence and debilitating effects, early detection of KOA remains elusive, often hindered by subjective diagnostic methods and the absence of reliable biomarkers. This research aims to address these challenges by leveraging deep learning techniques and ensemble methodologies for accurate KOA classification using knee X-ray images. This paper utilized a dataset sourced from the Osteoarthritis Initiative (OAI), comprising a large collection of knee X-ray images graded according to the Kellgren-Lawrence (KL) grading system. The proposed design methodology involves preprocessing the input X-ray images and training multiple pre-trained Convolutional Neural Network (CNN) models, including ResNet50, InceptionResNetV2, and Xception to classify KOA severity grades. Additionally, this work introduced an ensemble model by combining predictions from these base models to improve overall performance of the Computer-Aided Diagnosis (CAD) system. The obtained results demonstrate the effectiveness of the ensemble approach, outperforming individual algorithms in terms of accuracy, precision, recall, F1-score, and balanced accuracy. However, challenges persist in accurately distinguishing between adjacent KL grades, particularly grades#1 and #2, highlighting the need for further refinement. Notably, the proposed CAD model showcases superior predictive accuracy compared to various state-of-art methods, offering a promising avenue for early KOA diagnosis and personalized treatment strategies.

Automated Cardiovascular Disease Diagnosis from X-ray Images using Convolutional Neural Networks

Problem Cardiovascular Disease (CVD) is a leading cause of mortality worldwide, and its early and accurate diagnosis is crucial for effective treatment and patient care. Medical imaging, particularly X-ray imaging, plays a crucial role in the detection and assessment of cardiovascular abnormalities. In recent years, Convolutional Neural Networks (CNNs) have emerged as a powerful tool in medical image analysis, demonstrating promising results in various diagnostic tasks. Proposed Approach This research paper investigates the application of CNNs for the automated diagnosis of CVD from X-ray images. The CVD diagnosis framework proposed in this study consists of three key modules. The first module is an X-ray feature extractor built using a state-of-the-art CNN architecture. The second module is an age prediction component, which accurately estimates the age of the patients from the X-ray images. Finally, the third module is the CVD classifier, which categorizes the input X-ray images into four predefined severity categories of Cardiovascular Disease. Result Through extensive experiments, the proposed method has demonstrated its capability to offer novel insights into the potential use of X-ray images for predicting systemic biomarkers in the diagnosis of CVD. I expect that the proposed CVD diagnosis method can provide a significant advancement in the field of cardiovascular healthcare by offering an accurate, efficient, and automated solution for early detection of CVD.

A Multi-featured Approach by Integrating Digital Hand and Dental X-Ray for Human Age Estimation

Traditionally, human bone age is estimated manually by inspecting the multiple body part X-ray images, which is extremely time-consuming and prone to error. The accuracy of the human estimate depends on the experience of the medical practitioner, and thus it suffers from intra- and inter-observer variability. Hence, efficient automatic approaches are required to determine human age with high accuracy. In this work, we propose a human age estimation technique using Deep Learning (DL) technique based on hand X-ray images combined with dental orthopantomographs (OPGs) is proposed. Here, the input X-ray image is pre-processed first using Non-Local Means (NLM) first, followed by Region of Interest (RoI) extraction. Later, color and position image augmentation are performed in order to balance the dataset. Thereafter, the salient features in the image are determined, and based on these features, human age estimation is carried out using the Deep Residual Network (DRN). Here, the DRN is trained using the Beluga whale lion optimization (BWLO) algorithm. Furthermore, the BWLO_DRN is examined for its superiority considering the model accuracy and is found to obtain value of 90.1% on hand-wrist and 89.9% OPG real time dataset, thus showing superior performance for hand-wrist images.

COVID-19 Detection using Hybrid CNN-RNN Architecture with Transfer Learning from X-Rays.

Millions of people have been infected with COVID-19, which has spread quickly worldwide since the start of 2020, resulting in numerous fatalities. Identification of infected individuals is essential to control the spread of the virus. In this study, we propose a hybrid architecture that combines Convolutional Neural Networks (CNNs) with Recurrent Neural Networks (RNNs) and leverages transfer learning to enhance the accuracy of COVID-19 detection from X-ray images. The proposed work utilizes 4 pre-trained CNN architectures, namely, InceptionnetV3, Densenet121, Inception-ResNet V2, and VGG19, to extract high-level features from the input X-ray images. These features are then fed into the second component, an RNN-based network, which captures the temporal dependencies within the extracted features. To evaluate the performance of the proposed architecture, a comprehensive dataset consisting of X-ray images from COVID-19 positive cases, non-COVID-19 pneumonia cases, and healthy individuals is used. Gradient class activation map (Grad-CAM) analysis has been applied to the obtained results to provide heat-map pictures specific to each class and coloured visualizations of the COVID-19-infected areas in CXR images. Experimental results demonstrate that the proposed hybrid CNN-RNN architecture achieves promising results in COVID-19 detection from X-ray images. The model exhibits high accuracy, precision, recall, area under the receiver operating characteristics (ROC) curve (AUC), and F1-score, outperforming other state-of-the-art methods. The combination of CNNs and RNNs enables the model to effectively capture spatial and temporal information, leading to improved performance in COVID-19 detection. The proposed hybrid architecture with transfer learning from X-ray images provides a robust and efficient solution for COVID-19 detection. The model can potentially assist healthcare professionals in making accurate and timely diagnoses, thereby contributing to the global efforts to combat the COVID-19 pandemic. In the present work, VGG19-RNN architecture outperformed all other networks in terms of accuracy. The most effective training and validation accuracy for the VGG19-RNN architecture is 99% & 97.70%, respectively, and the loss was 0.02 & 0.09 at epoch 100.

Interpretable thoracic pathologic prediction via learning group-disentangled representation.

Deep learning has brought a significant progress in medical image analysis. However, their lack of interpretability might bring high risk for wrong diagnosis with limited clinical knowledge embedding. In other words, we believe it's crucial for humans to interpret how deep learning work for medical analysis, thus appropriately adding knowledge constraints to correct the bias of wrong results. With such purpose, we propose Representation Group-Disentangling Network (RGD-Net) to explain the process of feature extraction and decision making inside deep learning framework, where we completely disentangle feature space of input X-ray images into independent feature groups, and each group would contribute to diagnose of a specific disease. Specifically, we first state problem definition for interpretable prediction with auto-encoder structure. Then, group-disentangled representations are extracted from input X-ray images with the proposed Group-Disentangle Module, which constructs semantic latent space by enforcing semantic consistency of attributes. Afterwards, adversarial constricts on mapping from features to diseases are proposed to prevent model collapse during training. Finally, a novel design of local tuning medical application is proposed based on RGB-Net, which is capable to aid clinicians for reasonable diagnosis. By conducting quantity of experiments on public datasets, RGD-Net have been superior to comparative studies by leveraging potential factors contributing to different diseases. We believe our work could bring interpretability in digging inherent patterns of deep learning on medical image analysis.

Highly dynamic X-ray image enhancement based on generative adversarial network

When using X-ray to detect industrial workpieces, the images obtained frequently have low contrast, making it difficult to detect defects. This paper proposes a GAN-based X-ray image enhancement network to address this issue. In detail, the X-ray image is concatenated with its antiphase image (as an exposure mask) as the input image, and a trainable Sobel operator is used to extract the edge features of the input image. The input image and edge features are then concatenated and fed into the U-Net generator to be enhanced. The spatial and channel attention models are used to adjust feature weights in U-Net, and a detail extraction network is designed to extract detail features from the input X-ray image. Furthermore, the extracted detail features are fused with the image by the generator after contrast stretching to produce the final enhanced image. Finally, a global-local discriminator is used to discriminate the authenticity of the image so that the contrast of the final obtained image is improved and the details are highlighted. Following experimental validation, the method proposed in this paper has a significant enhancement effect on industrial X-ray images and performs well in terms of enhancing image contrast and highlighting image details.

HQDCNet: Hybrid Quantum Dilated Convolution Neural Network for detecting covid-19 in the context of Big Data Analytics.

Medical care services are changing to address problems with the development of big data frameworks as a result of the widespread use of big data analytics. Covid illness has recently been one of the leading causes of death in people. Since then, related input chest X-ray image for diagnosing COVID illness have been enhanced by diagnostic tools. Big data technological breakthroughs provide a fantastic option for reducing contagious Covid disease. To increase the model's confidence, it is necessary to integrate a large number of training sets, however handling the data may be difficult. With the development of big data technology, a unique method to identify and categorise covid illness is now found in this research. In order to manage incoming big data, a massive volume of chest x-ray images is gathered and analysed using a distributed computing server built on the Hadoop framework. In order to group identical groups in the input x-ray images, which in turn segments the dominating portions of an image, the fuzzy empowered weighted k-means algorithm is then employed. A hybrid quantum dilated convolution neural network is suggested to classify various kinds of covid instances, and a Black Widow-based Moth Flame is also shown to improve the performance of the classifier pattern. The performance analysis of COVID-19 detection makes use of the COVID-19 radiography dataset. The suggested HQDCNet approach has an accuracy of 99.01. The experimental results are evaluated in Python using performance metrics such as accuracy, precision, recall, f-measure, and loss function.

A GUI Based Application for Lung Cancer Diagnosis Using Comprehensive Analysis Deep Learning Approach

Abstract: Lung cancer is the third most terrible cancer in the world. It has been among the leading causes of death in both adults and children from recent years. Early cancer detection can lead to better treatment options for patients. The focus of conventional feature extraction methods is either on low -level features or high-level features, with some manually created features being utilized to fill in the gaps. A feature extraction framework does not require handcrafted features can be created to close this gap via encoding/combining low-level and high-level characteristics. Due to its ability to fully explain both low-level and high-level information and its integration of the feature extraction stage into the self-learning process, deep learning is incredibly effective for feature representation. Consequently, in this study, we use deep learning algorithms to detect the presence of lung cancer without the need for several doctor consultations. A web application is created as a healthcare application where lung cancer is detected using an input x-ray image. To identify cancer, the implementation uses the VGG-16 classification algorithm. As a result, the presence of the disease can be predicted early. We can then take quick action to stop any additional repercussions, which saves time, money, and human error. Lung cancer and its presence are identified in this investigation.

DTBV: A Deep Transfer-Based Bone Cancer Diagnosis System Using VGG16 Feature Extraction

Among the many different types of cancer, bone cancer is the most lethal and least prevalent. More cases are reported each year. Early diagnosis of bone cancer is crucial since it helps limit the spread of malignant cells and reduce mortality. The manual method of detection of bone cancer is cumbersome and requires specialized knowledge. A deep transfer-based bone cancer diagnosis (DTBV) system using VGG16 feature extraction is proposed to address these issues. The proposed DTBV system uses a transfer learning (TL) approach in which a pre-trained convolutional neural network (CNN) model is used to extract features from the pre-processed input image and a support vector machine (SVM) model is used to train using these features to distinguish between cancerous and healthy bone. The CNN is applied to the image datasets as it provides better image recognition with high accuracy when the layers in neural network feature extraction increase. In the proposed DTBV system, the VGG16 model extracts the features from the input X-ray image. A mutual information statistic that measures the dependency between the different features is then used to select the best features. This is the first time this method has been used for detecting bone cancer. Once selected features are selected, they are fed into the SVM classifier. The SVM model classifies the given testing dataset into malignant and benign categories. A comprehensive performance evaluation has demonstrated that the proposed DTBV system is highly efficient in detecting bone cancer, with an accuracy of 93.9%, which is more accurate than other existing systems.

Weakly supervised thoracic disease localization via disease masks

To enable a deep learning-based system to be used in the medical domain as a computer-aided diagnosis system, it is essential to not only classify diseases but also present the locations of the diseases. However, collecting instance-level annotations for various thoracic diseases is expensive. Therefore, weakly supervised localization methods have been proposed that use only image-level annotation. While the previous methods presented the disease location as the most discriminative part for classification, this causes a deep network to localize wrong areas for indistinguishable X-ray images. To solve this issue, we propose a spatial attention method using disease masks that describe the areas where diseases mainly occur. We then apply the spatial attention to find the precise disease area by highlighting the highest probability of disease occurrence. Meanwhile, the various sizes, rotations and noise in chest X-ray images make generating the disease masks challenging. To reduce the variation among images, we employ an alignment module to transform an input X-ray image into a generalized image. Through extensive experiments on the NIH-Chest X-ray dataset with eight kinds of diseases, we show that the proposed method results in superior localization performances compared to state-of-the-art methods.

A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images

IntroductionPneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance. ObjectivesA new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images. MethodsThe proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification scenarios. ResultsThe proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score. ConclusionThe proposed hybrid deep learning framework could provide promising and encouraging explainable identification performance comparing with the individual, ensemble models, or even the latest AI models in the literature. The code is available here: https://github.com/chiagoziemchima/Pneumonia_Identificaton.

Lung Cancer Detection using VGG NET 16 Architecture

Cancer is one of the main reason for loss of human life across the world. All the medical practitioners and researchers are dealing with the demanding situations to fight against cancer. Based on the report in 2019 from American Cancer Society, 96,480 deaths are anticipated due to skin cancers, 142,670 deaths are from lung cancers, 42,260 deaths are from breast cancers, 31,620 deaths are from prostate cancers, and 17,760 deaths are from mind cancers. Initial detection of most cancers has the pinnacle precedence for saving the lives. This paper proposed a lung cancer detection using Deep Learning based on VEE NET architecture. This was one of the famous models submitted to ILSVRC-2014. Visual checkup and manual practices are used on this venture for the various types of cancer diagnoses. This guide interpretation of scientific images that needs massive time intake and is notably susceptible to mistakes. Thus, in this project, we apply deep learning algorithms to identify lung cancer and its presence without the need for several consultations from different doctors. This leads to an earlier prediction of the presence of the disease and allows us to take prior actions immediately to avoid further consequences in an effective and cheap manner avoiding human error rate. In this project lung cancer and its presence is determined. A web application is developed as a hospital application where an input x-ray image is given to detect lung cancer.

A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis.

In the recent era, various diseases have severely affected the lifestyle of individuals, especially adults. Among these, bone diseases, including Knee Osteoarthritis (KOA), have a great impact on quality of life. KOA is a knee joint problem mainly produced due to decreased Articular Cartilage between femur and tibia bones, producing severe joint pain, effusion, joint movement constraints and gait anomalies. To address these issues, this study presents a novel KOA detection at early stages using deep learning-based feature extraction and classification. Firstly, the input X-ray images are preprocessed, and then the Region of Interest (ROI) is extracted through segmentation. Secondly, features are extracted from preprocessed X-ray images containing knee joint space width using hybrid feature descriptors such as Convolutional Neural Network (CNN) through Local Binary Patterns (LBP) and CNN using Histogram of oriented gradient (HOG). Low-level features are computed by HOG, while texture features are computed employing the LBP descriptor. Lastly, multi-class classifiers, that is, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN), are used for the classification of KOA according to the Kellgren–Lawrence (KL) system. The Kellgren–Lawrence system consists of Grade I, Grade II, Grade III, and Grade IV. Experimental evaluation is performed on various combinations of the proposed framework. The experimental results show that the HOG features descriptor provides approximately 97% accuracy for the early detection and classification of KOA for all four grades of KL.

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.

Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient’s death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).

COVID-19 detection and heatmap generation in chest x-ray images.

.Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images.Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result.Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively.Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.

Optimal Diagnosis of COVID-19 Based on Convolutional Neural Network and Red Fox Optimization Algorithm.

SARS-CoV-2 is a specific type of Coronavirus that was firstly reported in China in December 2019 and is the causative agent of coronavirus disease 2019 (COVID-19). In March 2020, this disease spread to different parts of the world causing a global pandemic. Although this disease is still increasing exponentially day by day, early diagnosis of this disease is very important to reduce the death rate and to reduce the prevalence of this pandemic. Since there are sometimes human errors by physicians in the diagnosis of this disease, using computer-aided diagnostic systems can be helpful to get more accurate results. In this paper, chest X-ray images have been examined using a new pipeline machine vision-based system to provide more accurate results. In the proposed method, after preprocessing the input X-ray images, the region of interest has been segmented. Then, a combined gray-level cooccurrence matrix (GLCM) and Discrete Wavelet Transform (DWT) features have been extracted from the processed images. Finally, an improved version of Convolutional Neural Network (CNN) based on the Red Fox Optimization algorithm is employed for the classification of the images based on the features. The proposed method is validated by performing to three datasets and its results are compared with some state-of-the-art methods. The final results show that the suggested method has proper efficiency toward the others for the diagnosis of COVID-19.

COVID-XR: A Web Management Platform for Coronavirus Detection on X-ray Chest Images

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. Its symptoms are similar to those of the common flu, including fever, cough, dyspnea, myalgia, and fatigue. Due to its rapid expansion globally, the World Health Organization (OMS) declared it a pandemic. The molecular test commonly used worldwide for direct detection of the virus is the RT-PCR test but it takes time to process and the materials used are scarce. In this work we propose: (a) The design and implementation of a deep neural network architecture for the detection of patients with COVID-19 using as input X-ray images of the chest; the architecture is made up of a feature extraction phase, that is, a pre-trained model VGG16 extracts the features of the image; then in the second phase, a multilayer neural network classifies into one of two particular classes (1: COVID, 0: NO COVID). (b) The implementation of a Web platform that allows interested people to use our architecture in a clear, simple and transparent way. The deep learning algorithm was implemented in Python with specific libraries for the design of neural networks, while the Web platform was implemented in PHP using the Laravel framework and MySQL database. We evaluate the performance of our proposal using the sensitivity, specificity and area under the curve (AUC) evaluation metrics, obtaining good results in very short computational times.

Neutrosophic Set-Based Caries Lesion Detection Method to Avoid Perception Error

Dental caries is an infectious oral disease. The monitoring of caries region boundary, in regular intervals, is important for treatment purpose. To detect dental caries lesion, most of the time dentists use X-ray images. Due to human brain perception, sometimes it is difficult to detect the caries lesion accurately by observing the X-ray image manually. In this work, a framework has been proposed to detect caries lesion automatically within the optimum time. Almost all caries detection methods from the radiographic images apply iterative methods upon the entire image to separate initially suspected regions. Then, further processing is performed on the separated regions. This method reduced huge computation efforts by avoiding applying iterative methods upon the entire input image. This method transforms the input X-ray image into its equivalent neutrosophic domain to obtain initially suspected region. We have used a custom feature named ‘weight’ for neutrosophication. This feature is calculated by fusing other features differently. Once the initial region is detected, it is examined further to test whether there exists any iso-center rings like the catchment basin. This is the most important property of caries lesions. After the suspected region is confirmed as caries lesion, then the caries boundary is detected using active contour technique. The advantage of this system is that it avoids repetitive iterations at the time of suspected region selection using neutrosophication. Repetitive iterations upon the entire picture dimension are a time-consuming job. The performance of the proposed research work is satisfactory; the average accuracy is above 92%.

Semi-supervised OTSU based hyperbolic tangent Gaussian kernel fuzzy C-mean clustering for dental radiographs segmentation

Dental periapical X-ray image (DXRI) segmentation is an important process to examine dental images towards diagnosing medical systems is an essential operation within practical dentistry for periodontitis recognition. However, traditional clustering algorithms in image processing frequently accept deficiencies in finding teeth sample boundaries and parameters. The presentation related to clustering is improved while further data produced with the user. In DXRI segmentation, semi supervised fuzzy clustering which is a new collective scheme. Initially, pre-processing is done for the input X-Ray image in order to minimize error. Specifically, Otsu’s method divide the dental X-Ray image into background and foreground regions. Here, the chosen FCM to separate the teeth regions commencing on the preceding steps. A Semi-supervised Hyperbolic Tangent Gaussian kernel Fuzzy C-Means algorithm (HTGkFCM) is preferred so as to increase an outcome that is optimum than compared to the traditional methods. So, current method is less sensitive to noise with robustness. Real datasets for the implementation on the proposed framework with cluster validity computation such as, Davies–Bouldin (DB), Segmentation Accuracy (SA), Simplified Silhouete Width Criterion (SSWC), processing time, PBM and Mean Absolute Error (MAE). So the proposed work has been enhanced in terms of 7%, 2%, 5%, 6%, 3% and 30% than the state-of-the-art works. Simulation outcome shows the quality of clustering in the framework have higher accuracy and more reliable than other clustering methods.

An effective teeth recognition method using label tree with cascade network structure

In this article, we apply the deep learning technique to medical field for the teeth detection and classification of dental periapical radiographs, which is important for the medical curing and postmortem identification. We detect teeth in an input X-ray image and distinguish them from different position. An adult usually has 32 teeth, and some of them are similar while others have very different shape. So there are 32 teeth position for us to recognize, which is a challenging task. Convolutional neural network is a popular method to do multi-class detection and classification, but it needs a lot of training data to get a good result if used directly. The lack of data is a common case in medical field due to patients’ privacy. In this work, limited to the available data, we propose a new method using label tree to give each tooth several labels and decompose the task, which can deal with the lack of data. Then use cascade network structure to do automatic identification on 32 teeth position, which uses several convolutional neural network as its basic module. Meanwhile, several key strategies are utilized to improve the detection and classification performance. Our method can deal with many complex cases such as X-ray images with tooth loss, decayed tooth and filled tooth, which frequently appear on patients. The experiments on our dataset show: for small training dataset, compared to the precision and recall by training a 33-classes (32 teeth and background) state-of-the-art convolutional neural network directly, the proposed approach reaches a high precision and recall of 95.8% and 96.1% in total, which is a big improvement in such a complex task.