Purpose This study aims to adapt and evaluate the performance of different state-of-the-art deep learning object detection methods to automatically identify esophageal adenocarcinoma (EAC) regions from high-definition white light endoscopy (HD-WLE) images. Method Several state-of-the-art object detection methods using Convolutional Neural Networks (CNNs) were adapted to automatically detect abnormal regions in the esophagus HD-WLE images, utilizing VGG’16 as the backbone architecture for feature extraction. Those methods are Regional-based Convolutional Neural Network (R-CNN), Fast R-CNN, Faster R-CNN and Single-Shot Multibox Detector (SSD). For the evaluation of the different methods, 100 images from 39 patients that have been manually annotated by five experienced clinicians as ground truth have been tested. Results Experimental results illustrate that the SSD and Faster R-CNN networks show promising results, and the SSD outperforms other methods achieving a sensitivity of 0.96, specificity of 0.92 and F-measure of 0.94. Additionally, the Average Recall Rate of the Faster R-CNN in locating the EAC region accurately is 0.83. Conclusion In this paper, recent deep learning object detection methods are adapted to detect esophageal abnormalities automatically. The evaluation of the methods proved its ability to locate abnormal regions in the esophagus from endoscopic images. The automatic detection is a crucial step that may help early detection and treatment of EAC and also can improve automatic tumor segmentation to monitor its growth and treatment outcome.

Early detection of esophageal abnormalities can help in preventing the progression of the disease into later stages. During esophagus examination, abnormalities are often overlooked due to the irregular shape, variable size and the complex surrounding area which requires a significant effort and experience. In this paper, a novel deep learning model which is based on Faster Region-Based Convolution Neural Network (Faster R-CNN) is presented to automatically detect abnormalities in the esophagus from endoscopic images. The proposed detection system is based on a combination of Gabor handcrafted features with CNN features. The Densely Connected Convolution Networks (DenseNets) architecture is embraced to extract CNN features providing a strengthened feature propagation between the layers and allay the vanishing gradient problem. To address the challenges of detecting abnormal complex regions, we propose fusing extracted Gabor features with CNN features through concatenation to enhance texture details in the detection stage. Our newly designed architecture is validated on two datasets (Kvasir and MICCAI 2015). Regarding the Kvasir, the results show an outstanding performance with a recall of 90.2% and precision of 92.1% with a mean of average precision (mAP) of 75.9%. While for the Miccai 2015 dataset, the model is able to surpass the state-of-the-art performance with 95% recall and 91% precision with mAP value of 84%. Experimental results demonstrate that the system is able to detect abnormalities in endoscopic images with good performance without any human intervention.

Esophageal cancer is categorized as a type of disease with a high mortality rate. Early detection of esophageal abnormalities (i.e. precancerous and early cancerous) can improve the survival rate of the patients. Recent deep learning-based methods for selected types of esophageal abnormality detection from endoscopic images have been proposed. However, no methods have been introduced in the literature to cover the detection from endoscopic videos, detection from challenging frames and detection of more than one esophageal abnormality type. In this paper, we present an efficient method to automatically detect different types of esophageal abnormalities from endoscopic videos. We propose a novel 3D Sequential DenseConvLstm network that extracts spatiotemporal features from the input video. Our network incorporates 3D Convolutional Neural Network (3DCNN) and Convolutional Lstm (ConvLstm) to efficiently learn short and long term spatiotemporal features. The generated feature map is utilized by a region proposal network and ROI pooling layer to produce a bounding box that detects abnormality regions in each frame throughout the video. Finally, we investigate a post-processing method named Frame Search Conditional Random Field (FS-CRF) that improves the overall performance of the model by recovering the missing regions in neighborhood frames within the same clip. We extensively validate our model on an endoscopic video dataset that includes a variety of esophageal abnormalities. Our model achieved high performance using different evaluation metrics showing 93.7% recall, 92.7% precision, and 93.2% F-measure. Moreover, as no results have been reported in the literature for the esophageal abnormality detection from endoscopic videos, to validate the robustness of our model, we have tested the model on a publicly available colonoscopy video dataset, achieving the polyp detection performance in a recall of 81.18%, precision of 96.45% and F-measure 88.16%, compared to the state-of-the-art results of 78.84% recall, 90.51% precision and 84.27% F-measure using the same dataset. This demonstrates that the proposed method can be adapted to different gastrointestinal endoscopic video applications with a promising performance.

Barrett’s esophagus (BE) is a premalignant condition that has an increased risk to turn into esophageal adenocarcinoma. Classification and staging of the different changes (BE in particular) in the esophageal mucosa are challenging since they have a very similar appearance. Confocal laser endomicroscopy (CLE) is one of the newest endoscopy tools that is commonly used to identify the pathology type of the suspected area of the esophageal mucosa. However, it requires a well-trained physician to classify the image obtained from CLE. An automatic stage classification of esophageal mucosa is presented. The proposed model enhances the internal features of CLE images using an image filter that combines fractional integration with differentiation. Various features are then extracted on a multiscale level, to classify the mucosal tissue into one of its four types: normal squamous (NS), gastric metaplasia (GM), intestinal metaplasia (IM or BE), and neoplasia. These sets of features are used to train two conventional classifiers: support vector machine (SVM) and random forest. The proposed method was evaluated on a dataset of 96 patients with 557 images of different histopathology types. The SVM classifier achieved the best performance with 96.05% accuracy based on a leave-one-patient-out cross-validation. Additionally, the dataset was divided into 60% training and 40% testing; the model achieved an accuracy of 93.72% for the testing data using the SVM. The presented model showed superior performance when compared with four state-of-the-art methods. Accurate classification is essential for the intestinal metaplasia grade, which most likely develops into esophageal cancer. Not only does our method come to the aid of physicians for more accurate diagnosis by acting as a second opinion, but it also acts as a training method for junior physicians who need practice in using CLE. Consequently, this work contributes to an automatic classification that facilitates early intervention and decreases samples of required biopsy.

Myositis is a rare muscle disorder resulting from the immune system’s attack on the muscle cells, leading to muscle weaknesses. A precise and early diagnosis of the illness is crucial for effective treatment. Differentiating among various types of Myositis remains a significant challenge, leading to false diagnoses and delayed treatment for IBM patients, which can cause muscular atrophy. This paper introduces a new Hybrid computer-aided model that can effectively differentiate among various categories: PolyMyositis (PM), DermatoMyositis (DM), and Inclusion Body Myositis (IBM). A novel method for detecting and classifying inflammatory myopathies from ultrasound images based on a modified lightweight YOLOv5 Architecture is proposed named YOLO-CSE. The proposed model utilizes the Convolutional Block Attention Module (CBAM) to focus on the significant minor extracted features in the image. Moreover, we reuse the extracted features by incorporating the Spatial Pyramid Pooling-Fast Plus (SPPF ⁺ ), which strengthens the feature extraction capability while providing maximum information flow in the network. Furthermore, the Exponential Linear Unit (ELU) is integrated to activate the function for more accurate results by naming this layer (SPPF ⁺ ELU). The performance is tested on large benchmark datasets consisting of 3214 muscle ultrasound images for 80 patients, divided into 14 DermatoMyositis (DM), 14 PolyMyositis (PM), 19 Inclusion Body Myositis (IBM) and 33 Normal patients. Extensive and detailed analysis has been tested on several stages to clarify the role of the CBAM and SPPF Plus modules in our model. Additionally, we analyze the model’s performance through four different suggested classification strategies (binary and multiclass classification). Data Augmentation was proposed to suppress the dataset’s imbalance and boost the model’s performance. Mosaic data augmentation enhanced the dataset by randomly cropping, arranging and scaling images. Various techniques of augmentation were employed, including flipping, cropping, rotating, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Median Blur filters. Experiments have been conducted to demonstrate the effectiveness of the proposed system. The proposed YOLO-CSE achieved highly acceptable accuracy, precision, recall, and F1 Score for the different strategies. The suggested model achieved an average accuracy for binary and multiclass classification above 98% and average precision above 96%. The results demonstrate that the proposed model successfully detects and classifies inflammatory myopathies with high accuracy.