Fine-tuning Pre-trained Convolutional Neural Networks for Gastric Precancerous Disease Classification on Magnification Narrow-band Imaging Images

Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset

Gastrointestinal (GI) disorders, encompassing conditions like cancer and Crohn's disease, pose a significant threat to public health. Endoscopic examinations have become crucial for diagnosing and treating these disorders efficiently. However, the subjective nature of manual evaluations by gastroenterologists can lead to potential errors in disease classification. In addition, the difficulty of diagnosing diseased tissues in GI and the high similarity between classes made the subject a difficult area. Automated classification systems that use artificial intelligence to solve these problems have gained traction. Automatic detection of diseases in medical images greatly benefits in the diagnosis of diseases and reduces the time of disease detection. In this study, we suggested a new architecture to enable research on computer-assisted diagnosis and automated disease detection in GI diseases. This architecture, called Spatial-Attention ConvMixer (SAC), further developed the patch extraction technique used as the basis of the ConvMixer architecture with a spatial attention mechanism (SAM). The SAM enables the network to concentrate selectively on the most informative areas, assigning importance to each spatial location within the feature maps. We employ the Kvasir dataset to assess the accuracy of classifying GI illnesses using the SAC architecture. We compare our architecture's results with Vanilla ViT, Swin Transformer, ConvMixer, MLPMixer, ResNet50, and SqueezeNet models. Our SAC method gets 93.37% accuracy, while the other architectures get respectively 79.52%, 74.52%, 92.48%, 63.04%, 87.44%, and 85.59%. The proposed spatial attention block improves the accuracy of the ConvMixer architecture on the Kvasir, outperforming the state-of-the-art methods with an accuracy rate of 93.37%.

Medical image identification methods: A review

The identification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Medical image data mainly include electronic health record data and gene information data, etc. Although intelligent imaging provided a good scheme for medical image analysis over traditional methods that rely on the handcrafted features, it remains challenging due to the diversity of imaging modalities and clinical pathologies. Many medical image identification methods provide a good scheme for medical image analysis. The concepts pertinent of methods, such as the machine learning, deep learning, convolutional neural networks, transfer learning, and other image processing technologies for medical image are analyzed and summarized in this paper. We reviewed these recent studies to provide a comprehensive overview of applying these methods in various medical image analysis tasks, such as object detection, image classification, image registration, segmentation, and other tasks. Especially, we emphasized the latest progress and contributions of different methods in medical image analysis, which are summarized base on different application scenarios, including classification, segmentation, detection, and image registration. In addition, the applications of different methods are summarized in different application area, such as pulmonary, brain, digital pathology, brain, skin, lung, renal, breast, neuromyelitis, vertebrae, and musculoskeletal, etc. Critical discussion of open challenges and directions for future research are finally summarized. Especially, excellent algorithms in computer vision, natural language processing, and unmanned driving will be applied to medical image recognition in the future.

Exploration and Enhancement of Classifiers in the Detection of Lung Cancer from Histopathological Images

Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. The primary objective of this work is to propose a novel approach for the detection of lung cancer using histopathological images. In this work, the histopathological images underwent preprocessing, followed by segmentation using a modified approach of KFCM-based segmentation and the segmented image intensity values were dimensionally reduced using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Algorithms such as KL Divergence and Invasive Weed Optimization (IWO) are used for feature selection. Seven different classifiers such as SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without feature selection and hyperparameter tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%.

Lightweight deep learning model incorporating an attention mechanism and feature fusion for automatic classification of gastric lesions in gastroscopic images

Accurate diagnosis of various lesions in the formation stage of gastric cancer is an important problem for doctors. Automatic diagnosis tools based on deep learning can help doctors improve the accuracy of gastric lesion diagnosis. Most of the existing deep learning-based methods have been used to detect a limited number of lesions in the formation stage of gastric cancer, and the classification accuracy needs to be improved. To this end, this study proposed an attention mechanism feature fusion deep learning model with only 14 million (M) parameters. Based on that model, the automatic classification of a wide range of lesions covering the stage of gastric cancer formation was investigated, including non-neoplasm(including gastritis and intestinal metaplasia), low-grade intraepithelial neoplasia, and early gastric cancer (including high-grade intraepithelial neoplasia and early gastric cancer). 4455 magnification endoscopy with narrow-band imaging(ME-NBI) images from 1188 patients were collected to train and test the proposed method. The results of the test dataset showed that compared with the advanced gastric lesions classification method with the best performance (overall accuracy = 94.3%, parameters = 23.9 M), the proposed method achieved both higher overall accuracy and a relatively lightweight model (overall accuracy =95.6%, parameter = 14 M). The accuracy, sensitivity, and specificity of low-grade intraepithelial neoplasia were 94.5%, 93.0%, and 96.5%, respectively, achieving state-of-the-art classification performance. In conclusion, our method has demonstrated its potential in diagnosing various lesions at the stage of gastric cancer formation.

A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging

Narrow band imaging (NBI) has been extensively utilized as a diagnostic tool for colorectal neoplastic lesions. This study aimed to develop a trial deep learning (DL) based four-class classification model for low-grade dysplasia (LGD); high-grade dysplasia or mucosal carcinoma (HGD); superficially invasive submucosal carcinoma (SMs) and deeply invasive submucosal carcinomas (SMd) and evaluate its potential as a diagnostic tool. We collected a total of 1,390 NBI images as the dataset, including 53 LGD, 120 HGD, 20 SMs and 17 SMd. A total of 598,801 patches were trimmed from the lesion and background. A patch-based classification model was built by employing a residual convolutional neural network (CNN) and validated by three-fold cross-validation. The patch-based validation accuracy was 0.876, 0.957, 0.907 and 0.929 in LGD, HGD, SMs and SMd, respectively. The image-level classification algorithm was derived from the patch-based mapping across the entire image domain, attaining accuracies of 0.983, 0.990, 0.964, and 0.992 in LGD, HGD, SMs, and SMd, respectively. Our CNN-based model demonstrated high performance for categorizing the histological grade of dysplasia as well as the depth of invasion in routine colonoscopy, suggesting a potential diagnostic tool with minimal human inputs.

CLELNet: A continual learning network for esophageal lesion analysis on endoscopic images

BACKGROUND AND OBJECTIVE

A deep learning-based intelligent diagnosis system can significantly reduce the burden of endoscopists in the daily analysis of esophageal lesions. Considering the need to add new tasks in the diagnosis system, a deep learning model that can train a series of tasks incrementally using endoscopic images is essential for identifying the types and regions of esophageal lesions.

METHOD

In this paper, we proposed a continual learning-based esophageal lesion network (CLELNet), in which a convolutional autoencoder was designed to extract representation features of endoscopic images among different esophageal lesions. The proposed CLELNet consists of shared layers and task-specific layers. Shared layers are used to extract common features among different lesions while task-specific layers can complete different tasks. The first two tasks trained by the CLELNet are the classification (task 1) and the segmentation (task 2). We collected a dataset of esophageal endoscopic images from Macau Kiang Wu Hospital for training and testing the CLELNet.

RESULTS

The experimental results showed that the classification accuracy of task 1 was 95.96%, and the Intersection Over Union and the Dice Similarity Coefficient of task 2 were 65.66% and 78.08%, respectively.

CONCLUSIONS

The proposed CLELNet can realize task-incremental learning without forgetting the previous tasks and thus become a useful computer-aided diagnosis system in esophageal lesions analysis.

Transformer-based multi-task learning for classification and segmentation of gastrointestinal tract endoscopic images

Despite being widely utilized to help endoscopists identify gastrointestinal (GI) tract diseases using classification and segmentation, models based on convolutional neural network (CNN) have difficulties in distinguishing the similarities among some ambiguous types of lesions presented in endoscopic images, and in the training when lacking labeled datasets. Those will prevent CNN from further improving the accuracy of diagnosis. To address these challenges, we first proposed a Multi-task Network (TransMT-Net) capable of simultaneously learning two tasks (classification and segmentation), which has the transformer designed to learn global features and can combine the advantages of CNN in learning local features so that to achieve a more accurate prediction in identifying the lesion types and regions in GI tract endoscopic images. We further adopted the active learning in TransMT-Net to tackle the labeled image-hungry problem. A dataset was created from the CVC-ClinicDB dataset, Macau Kiang Wu Hospital, and Zhongshan Hospital to evaluate the model performance. Then, the experimental results show that our model not only achieved 96.94% accuracy in the classification task and 77.76% Dice Similarity Coefficient in the segmentation task but also outperformed those of other models on our test set. Meanwhile, active learning also produced positive results for the performance of our model with a small-scale initial training set, and even its performance with 30% of the initial training set was comparable to that of most comparable models with the full training set. Consequently, the proposed TransMT-Net has demonstrated its potential performance in GI tract endoscopic images and it through active learning can alleviate the shortage of labeled images.

Alzheimer Disease Classification through Transfer Learning Approach

Alzheimer's disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer's disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, we discuss the segmentation and classification of the Magnetic resonance imaging (MRI) of Alzheimer's disease, through the concept of transfer learning and customizing of the convolutional neural network (CNN) by specifically using images that are segmented by the Gray Matter (GM) of the brain. Instead of training and computing the proposed model accuracy from the start, we used a pre-trained deep learning model as our base model, and, after that, transfer learning was applied. The accuracy of the proposed model was tested over a different number of epochs, 10, 25, and 50. The overall accuracy of the proposed model was 97.84%.

Interpretable Classification of Tauopathies with a Convolutional Neural Network Pipeline Using Transfer Learning and Validation against Post-Mortem Clinical Cases of Alzheimer's Disease and Progressive Supranuclear Palsy

Neurodegenerative diseases, tauopathies, constitute a serious global health problem. The etiology of these diseases is unclear and an increase in their incidence has been projected in the next 30 years. Therefore, the study of the molecular mechanisms that might stop these neurodegenerative processes is very relevant. Classification of neurodegenerative diseases using Machine and Deep Learning algorithms has been widely studied for medical imaging such as Magnetic Resonance Imaging. However, post-mortem immunofluorescence imaging studies of the brains of patients have not yet been used for this purpose. These studies may represent a valuable tool for monitoring aberrant chemical changes or pathological post-translational modifications of the Tau polypeptide. We propose a Convolutional Neural Network pipeline for the classification of Tau pathology of Alzheimer's disease and Progressive Supranuclear Palsy by analyzing post-mortem immunofluorescence images with different Tau biomarkers performed with models generated with the architecture ResNet-IFT using Transfer Learning. These models' outputs were interpreted with interpretability algorithms such as Guided Grad-CAM and Occlusion Analysis. To determine the best classifier, four different architectures were tested. We demonstrated that our design was able to classify diseases with an accuracy of 98.41% on average whilst providing an interpretation concerning the proper classification involving different structural patterns in the immunoreactivity of the Tau protein in NFTs present in the brains of patients with Progressive Supranuclear Palsy and Alzheimer's disease.

Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images

Liver cancer is a malignant tumor with high morbidity and mortality, which has a tremendous negative impact on human survival. However, it is a challenging task to recognize tens of thousands of histopathological images of liver cancer by naked eye, which poses numerous challenges to inexperienced clinicians. In addition, factors such as long time-consuming, tedious work and huge number of images impose a great burden on clinical diagnosis. Therefore, our study combines convolutional neural networks with histopathology images and adopts a feature fusion approach to help clinicians efficiently discriminate the differentiation types of primary hepatocellular carcinoma histopathology images, thus improving their diagnostic efficiency and relieving their work pressure. In this study, for the first time, 73 patients with different differentiation types of primary liver cancer tumors were classified. We performed an adequate classification evaluation of liver cancer differentiation types using four pre-trained deep convolutional neural networks and nine different machine learning (ML) classifiers on a dataset of liver cancer histopathology images with multiple differentiation types. And the test set accuracy, validation set accuracy, running time with different strategies, precision, recall and F1 value were used for adequate comparative evaluation. Proved by experimental results, fusion networks (FuNet) structure is a good choice, which covers both channel attention and spatial attention, and suppresses channel interference with less information. Meanwhile, it can clarify the importance of each spatial location by learning the weights of different locations in space, then apply it to the study of classification of multi-differentiated types of liver cancer. In addition, in most cases, the Stacking-based integrated learning classifier outperforms other ML classifiers in the classification task of multi-differentiation types of liver cancer with the FuNet fusion strategy after dimensionality reduction of the fused features by principle component analysis (PCA) features, and a satisfactory result of 72.46% is achieved in the test set, which has certain practicality.

Identification of gastric cancer with convolutional neural networks: a systematic review

The identification of diseases is inseparable from artificial intelligence. As an important branch of artificial intelligence, convolutional neural networks play an important role in the identification of gastric cancer. We conducted a systematic review to summarize the current applications of convolutional neural networks in the gastric cancer identification. The original articles published in Embase, Cochrane Library, PubMed and Web of Science database were systematically retrieved according to relevant keywords. Data were extracted from published papers. A total of 27 articles were retrieved for the identification of gastric cancer using medical images. Among them, 19 articles were applied in endoscopic images and 8 articles were applied in pathological images. 16 studies explored the performance of gastric cancer detection, 7 studies explored the performance of gastric cancer classification, 2 studies reported the performance of gastric cancer segmentation and 2 studies analyzed the performance of gastric cancer delineating margins. The convolutional neural network structures involved in the research included AlexNet, ResNet, VGG, Inception, DenseNet and Deeplab, etc. The accuracy of studies was 77.3 - 98.7%. Good performances of the systems based on convolutional neural networks have been showed in the identification of gastric cancer. Artificial intelligence is expected to provide more accurate information and efficient judgments for doctors to diagnose diseases in clinical work.

Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model

It is challenging for endoscopists to accurately detect esophageal lesions during gastrointestinal endoscopic screening due to visual similarities among different lesions in terms of shape, size, and texture among patients. Additionally, endoscopists are busy fighting esophageal lesions every day, hence the need to develop a computer-aided diagnostic tool to classify and segment the lesions at endoscopic images to reduce their burden. Therefore, we propose a multi-task classification and segmentation (MTCS) model, including the Esophageal Lesions Classification Network (ELCNet) and Esophageal Lesions Segmentation Network (ELSNet). The ELCNet was used to classify types of esophageal lesions, and the ELSNet was used to identify lesion regions. We created a dataset by collecting 805 esophageal images from 255 patients and 198 images from 64 patients to train and evaluate the MTCS model. Compared with other methods, the proposed not only achieved a high accuracy (93.43%) in classification but achieved a dice similarity coefficient (77.84%) in segmentation. In conclusion, the MTCS model can boost the performance of endoscopists in the detection of esophageal lesions as it can accurately multi-classify and segment the lesions and is a potential assistant for endoscopists to reduce the risk of oversight.

Deep learning for gastroscopic images: computer-aided techniques for clinicians

Gastric disease is a major health problem worldwide. Gastroscopy is the main method and the gold standard used to screen and diagnose many gastric diseases. However, several factors, such as the experience and fatigue of endoscopists, limit its performance. With recent advancements in deep learning, an increasing number of studies have used this technology to provide on-site assistance during real-time gastroscopy. This review summarizes the latest publications on deep learning applications in overcoming disease-related and nondisease-related gastroscopy challenges. The former aims to help endoscopists find lesions and characterize them when they appear in the view shed of the gastroscope. The purpose of the latter is to avoid missing lesions due to poor-quality frames, incomplete inspection coverage of gastroscopy, etc., thus improving the quality of gastroscopy. This study aims to provide technical guidance and a comprehensive perspective for physicians to understand deep learning technology in gastroscopy. Some key issues to be handled before the clinical application of deep learning technology and the future direction of disease-related and nondisease-related applications of deep learning to gastroscopy are discussed herein.

Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention

The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.

Evaluation of the effects of an artificial intelligence system on endoscopy quality and preliminary testing of its performance in detecting early gastric cancer: a randomized controlled trial

BACKGROUND

Esophagogastroduodenoscopy (EGD) is a prerequisite for detecting upper gastrointestinal lesions especially early gastric cancer (EGC). An artificial intelligence system has been shown to monitor blind spots during EGD. In this study, we updated the system (ENDOANGEL), verified its effectiveness in improving endoscopy quality, and pretested its performance in detecting EGC in a multicenter randomized controlled trial.

METHODS

ENDOANGEL was developed using deep convolutional neural networks and deep reinforcement learning. Patients undergoing EGD in five hospitals were randomly assigned to the ENDOANGEL-assisted group or to a control group without use of ENDOANGEL. The primary outcome was the number of blind spots. Secondary outcomes included performance of ENDOANGEL in predicting EGC in a clinical setting.

RESULTS

1050 patients were randomized, and 498 and 504 patients in the ENDOANGEL and control groups, respectively, were analyzed. Compared with the control group, the ENDOANGEL group had fewer blind spots (mean 5.38 [standard deviation (SD) 4.32] vs. 9.82 [SD 4.98]; P < 0.001) and longer inspection time (5.40 [SD 3.82] vs. 4.38 [SD 3.91] minutes; P < 0.001). In the ENDOANGEL group, 196 gastric lesions with pathological results were identified. ENDOANGEL correctly predicted all three EGCs (one mucosal carcinoma and two high grade neoplasias) and two advanced gastric cancers, with a per-lesion accuracy of 84.7 %, sensitivity of 100 %, and specificity of 84.3 % for detecting gastric cancer.

CONCLUSIONS

In this multicenter study, ENDOANGEL was an effective and robust system to improve the quality of EGD and has the potential to detect EGC in real time.

Gastrointestinal Disease Classification in Endoscopic Images Using Attention-Guided Convolutional Neural Networks

Gastrointestinal (GI) diseases constitute a leading problem in the human digestive system. Consequently, several studies have explored automatic classification of GI diseases as a means of minimizing the burden on clinicians and improving patient outcomes, for both diagnostic and treatment purposes. The challenge in using deep learning-based (DL) approaches, specifically a convolutional neural network (CNN), is that spatial information is not fully utilized due to the inherent mechanism of CNNs. This paper proposes the application of spatial factors in improving classification performance. Specifically, we propose a deep CNN-based spatial attention mechanism for the classification of GI diseases, implemented with encoder–decoder layers. To overcome the data imbalance problem, we adapt data-augmentation techniques. A total of 12,147 multi-sited, multi-diseased GI images, drawn from publicly available and private sources, were used to validate the proposed approach. Furthermore, a five-fold cross-validation approach was adopted to minimize inconsistencies in intra- and inter-class variability and to ensure that results were robustly assessed. Our results, compared with other state-of-the-art models in terms of mean accuracy (ResNet50 = 90.28, GoogLeNet = 91.38, DenseNets = 91.60, and baseline = 92.84), demonstrated better outcomes (Precision = 92.8, Recall = 92.7, F1-score = 92.8, and Accuracy = 93.19). We also implemented t-distributed stochastic neighbor embedding (t–SNE) and confusion matrix analysis techniques for better visualization and performance validation. Overall, the results showed that the attention mechanism improved the automatic classification of multi-sited GI disease images. We validated clinical tests based on the proposed method by overcoming previous limitations, with the goal of improving automatic classification accuracy in future work.

Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning

The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patients with follow-up treatment plans and preventing lesion deterioration. In recent years, deep learning has achieved many satisfactory results in gastroscopic image classification tasks. However, most of them need a training set that consists of large numbers of images labeled by experienced experts. To reduce the image annotation burdens and improve the classification ability on small labeled gastroscopic image datasets, this study proposed a novel semi-supervised efficient contrastive learning (SSECL) classification method for esophageal disease. First, an efficient contrastive pair generation (ECPG) module was proposed to generate efficient contrastive pairs (ECPs), which took advantage of the high similarity features of images from the same lesion. Then, an unsupervised visual feature representation containing the general feature of esophageal gastroscopic images is learned by unsupervised efficient contrastive learning (UECL). At last, the feature representation will be transferred to the down-stream esophageal disease classification task. The experimental results have demonstrated that the classification accuracy of SSECL is 92.57%, which is better than that of the other state-of-the-art semi-supervised methods and is also higher than the classification method based on transfer learning (TL) by 2.28%. Thus, SSECL has solved the challenging problem of improving the classification result on small gastroscopic image dataset by fully utilizing the unlabeled gastroscopic images and the high similarity information among images from the same lesion. It also brings new insights into medical image classification tasks.

StyleGANs and Transfer Learning for Generating Synthetic Images in Industrial Applications

Deep learning applications on computer vision involve the use of large-volume and representative data to obtain state-of-the-art results due to the massive number of parameters to optimise in deep models. However, data are limited with asymmetric distributions in industrial applications due to rare cases, legal restrictions, and high image-acquisition costs. Data augmentation based on deep learning generative adversarial networks, such as StyleGAN, has arisen as a way to create training data with symmetric distributions that may improve the generalisation capability of built models. StyleGAN generates highly realistic images in a variety of domains as a data augmentation strategy but requires a large amount of data to build image generators. Thus, transfer learning in conjunction with generative models are used to build models with small datasets. However, there are no reports on the impact of pre-trained generative models, using transfer learning. In this paper, we evaluate a StyleGAN generative model with transfer learning on different application domains—training with paintings, portraits, Pokémon, bedrooms, and cats—to generate target images with different levels of content variability: bean seeds (low variability), faces of subjects between 5 and 19 years old (medium variability), and charcoal (high variability). We used the first version of StyleGAN due to the large number of publicly available pre-trained models. The Fréchet Inception Distance was used for evaluating the quality of synthetic images. We found that StyleGAN with transfer learning produced good quality images, being an alternative for generating realistic synthetic images in the evaluated domains.

Assessment of hip displacement in children with cerebral palsy using machine learning approach

Manual measurements of migration percentage (MP) on pelvis radiographs for assessing hip displacement are subjective and time consuming. A deep learning approach using convolution neural networks (CNNs) to automatically measure the MP was proposed. The pre-trained Inception ResNet v2 was fine tuned to detect locations of the eight reference landmarks used for MP measurements. A second network, fine-tuned MobileNetV2, was trained on the regions of interest to obtain more precise landmarks' coordinates. The MP was calculated from the final estimated landmarks' locations. A total of 122 radiographs were divided into 57 for training, 10 for validation, and 55 for testing. The mean absolute difference (MAD) and intra-class correlation coefficient (ICC [2,1]) of the comparison for the MP on 110 measurements (left and right hips) were 4.5 [Formula: see text] 4.3% (95% CI, 3.7-5.3%) and 0.91, respectively. Sensitivity and specificity were 87.8% and 93.4% for the classification of hip displacement (MP-threshold of 30%), and 63.2% and 94.5% for the classification of surgery-needed hips (MP-threshold of 40%). The prediction results were returned within 5 s. The developed fine-tuned CNNs detected the landmarks and provided automatic MP measurements with high accuracy and excellent reliability, which can assist clinicians to diagnose hip displacement in children with CP.

Automatic classification of esophageal disease in gastroscopic images using an efficient channel attention deep dense convolutional neural network

The accurate diagnosis of various esophageal diseases at different stages is crucial for providing precision therapy planning and improving 5-year survival rate of esophageal cancer patients. Automatic classification of various esophageal diseases in gastroscopic images can assist doctors to improve the diagnosis efficiency and accuracy. The existing deep learning-based classification method can only classify very few categories of esophageal diseases at the same time. Hence, we proposed a novel efficient channel attention deep dense convolutional neural network (ECA-DDCNN), which can classify the esophageal gastroscopic images into four main categories including normal esophagus (NE), precancerous esophageal diseases (PEDs), early esophageal cancer (EEC) and advanced esophageal cancer (AEC), covering six common sub-categories of esophageal diseases and one normal esophagus (seven sub-categories). In total, 20,965 gastroscopic images were collected from 4,077 patients and used to train and test our proposed method. Extensive experiments results have demonstrated convincingly that our proposed ECA-DDCNN outperforms the other state-of-art methods. The classification accuracy (Acc) of our method is 90.63% and the averaged area under curve (AUC) is 0.9877. Compared with other state-of-art methods, our method shows better performance in the classification of various esophageal disease. Particularly for these esophageal diseases with similar mucosal features, our method also achieves higher true positive (TP) rates. In conclusion, our proposed classification method has confirmed its potential ability in a wide variety of esophageal disease diagnosis.

3D-semantic segmentation and classification of stomach infections using uncertainty aware deep neural networks

Wireless capsule endoscopy (WCE) might move through human body and captures the small bowel and captures the video and require the analysis of all frames of video due to which the diagnosis of gastrointestinal infections by the physician is a tedious task. This tiresome assignment has fuelled the researcher’s efforts to present an automated technique for gastrointestinal infections detection. The segmentation of stomach infections is a challenging task because the lesion region having low contrast and irregular shape and size. To handle this challenging task, in this research work a new deep semantic segmentation model is suggested for 3D-segmentation of the different types of stomach infections. In the segmentation model, deep labv3 is employed as a backbone of the ResNet-50 model. The model is trained with ground-masks and accurately performs pixel-wise classification in the testing phase. Similarity among the different types of stomach lesions accurate classification is a difficult task, which is addressed in this reported research by extracting deep features from global input images using a pre-trained ResNet-50 model. Furthermore, the latest advances in the estimation of uncertainty and model interpretability in the classification of different types of stomach infections is presented. The classification results estimate uncertainty related to the vital features in input and show how uncertainty and interpretability might be modeled in ResNet-50 for the classification of the different types of stomach infections. The proposed model achieved up to 90% prediction scores to authenticate the method performance.

SCNET: A Novel UGI Cancer Screening Framework Based on Semantic-Level Multimodal Data Fusion

Upper gastrointestinal (UGI) cancer has been identified as one of the ten most common causes of cancer deaths globally. UGI cancer screening is critical to improving the survival rate of UGI cancer patients. While many approaches to UGI cancer screening rely on single-modality data such as gastroscope imaging, limited studies have been dedicated to UGI cancer screening exploiting multisource and multimodal medical data, which could potentially lead to improved screening results. In this paper, we propose semantic-level cancer-screening network (SCNET), a framework for UGI cancer screening based on semantic-level multimodal upper gastrointestinal data fusion. Specifically, the proposed SCNET consists of a gastrointestinal image recognition flow and a textual medical record processing flow. High-level features of upper gastrointestinal data are extracted by identifying effective feature channels according to the correlation between the textual features and the spatial structure of the image features. The final screening results are obtained after the data fusion step. The experimental results show that the improvement of our approach over the state-of-the-art ones reached 4.01% in average. The source code of SCNET is available at https://github.com/netflymachine/SCNET.