ResNet Architecture Research Articles

Thermal Image Classification of Autistic Children Using Res-Net Architecture

The thermal Image Classification Method has been widely used for significant applications in many fields, including thermal images of the face. This study presents a method for thermal facial classification in children with autism spectrum disorder (ASD). Children with ASD have a neurological disorder that affects communication skills essential in daily life and often causes difficulties in social situations. As we know, the diagnosis of ASD currently still relies on human methods and does not yet have definite biological markers. Early diagnosis of ASD has a significant positive impact, especially in children. Deep learning techniques, especially in facial medical image analysis, have become a new research focus in ASD detection. Initial screening using a Convolutional Neural Network (CNN) model with a transfer learning approach offers great potential for early diagnosis of ASD. The use of thermal imaging as a passive method to analyze ASD-related physiological signals has been proposed. In previous research, a deep learning model was developed to classify the faces of autistic children using thermal images. Therefore, this study aims to create a new Thermal Image Classification model for Autistic Children Using Res-Net Architecture. The architectures applied are ResNet-18, ResNet-34, and ResNet-50. As a comparison system, several of the same parameter values are used: epoch 100, batch size 2, SGD, Cross-entropy, learning rate 0.001, and momentum 0.9. The study test results show that the results of ResNet-18 are 97.22%, ResNet-34 99.22%, and ResNet-50 99.41%. Based on these results, ResNet-50 has the highest value.

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types

Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of renal cell carcinoma subtypes. Nonetheless, the broader categorization of renal tissue into non-neoplastic normal tissue, benign tumor and malignant tumor remains understudied. This gap in research can primarily be attributed to the limited availability of extensive datasets including benign tumor and normal tissue in addition to specific type of renal cell carcinoma, which hampers the ability to conduct comprehensive studies in these broader categories. This research introduces a model aimed at classifying renal tissue into three primary categories: normal (non-neoplastic), benign tumor, and malignant tumor. Utilizing digital pathology while slide images (WSIs) from nephrectomy specimens of 2,535 patients from multiple institutions, the model provides a foundational approach for distinguishing these key tissue types. The study utilized a dataset of 12,223 WSIs comprising 1,300 WSIs of normal tissue, 700 WSIs of benign tumors, and 10,223 WSIs of malignant tumors. Employing the ResNet-18 architecture and a Multiple Instance Learning approach, the model demonstrated high accuracy, with F1-scores of 0.934 (CI: 0.933–0.934) for normal tissue, 0.684 (CI: 0.682–0.687) for benign tumors, and 0.878 (CI: 0.877–0.879) for malignant tumors. The overall performance was also notable, achieving a weighted average F1-score of 0.879 (CI: 0.879–0.880) and a weighted average area under the receiver operating characteristic curve of 0.969 (CI: 0.969–0.969). This model significantly aids in the swift and accurate diagnosis of renal tissue, encompassing non-neoplastic normal tissue, benign tumor, and malignant tumor.

Predicting the risk of type 2 diabetes mellitus (T2DM) emergence in 5 years using mammography images: a comparison study between radiomics and deep learning algorithm.

The prevalence of type 2 diabetes mellitus (T2DM) has been steadily increasing over the years. We aim to predict the occurrence of T2DM using mammography images within 5 years using two different methods and compare their performance. We examined 312 samples, including 110 positive cases (developed T2DM after 5 years) and 202 negative cases (did not develop T2DM) using two different methods. In the first method, a radiomics-based approach, we utilized radiomics features and machine learning (ML) algorithms. The entire breast region was chosen as the region of interest for extracting radiomics features. Then, a binary breast image was created from which we extracted 668 features and analyzed them using various ML algorithms. In the second method, a complex convolutional neural network (CNN) with a modified ResNet architecture and various kernel sizes was applied to raw mammography images for the prediction task. A nested, stratified five-fold cross-validation was done for both parts A and B to compute accuracy, sensitivity, specificity, and area under the receiver operating curve (AUROC). Hyperparameter tuning was also done to enhance the model's performance and reliability. The radiomics approach's light gradient boosting model gave 68.9% accuracy, 30.7% sensitivity, 89.5% specificity, and 0.63 AUROC. The CNN method achieved an AUROC of 0.58 over 20 epochs. Radiomics outperformed CNN by 0.05 in terms of AUROC. This may be due to the more straightforward interpretability and clinical relevance of predefined radiomics features compared with the complex, abstract features learned by CNNs.

Resnet for Blood Sample Detection: A Study on Improving Diagnostic Accuracy

Automated blood cell analysis plays a crucial role in medical diagnostics, enabling rapid and accurate assessment of a patient's health status. In this paper, we provide a unique technique for detecting and classifying WBCs,RBCs, and platelets inside blood smear pictures using ResNet (Residual Neural Network), a deep learning architecture. Because of its capacity to efficiently train very deep neural networks while minimizing the vanishing gradient problem, the ResNet architecture has exhibited excellent performance in a variety of image recognition applications. Leveraging the power of ResNet, we developed a multi-class classification model capable of distinguishing between WBCs, RBCs, and platelets within microscopic images of blood smears. Our methodology involved preprocessing the blood smear images to enhance contrast and remove noise, followed by image segmentation to isolate individual blood cells and platelets. The segmented images were then used to train and fine-tune a ResNet model, utilizing a large annotated dataset of labeled blood cell images. The trained model exhibited remarkable accuracy in identifying and classifying different blood cell types, even in the presence of overlapping cells or artifacts. We extensively tested our suggested technique, on a range of blood smear images to evaluate its performance. The findings demonstrated that ResNet effectively identifies and categorizes WBCs, (RBCs) and platelets. When compared to methods our approach showcased superior accuracy, robustness and generalization capabilities. After training the model with the Resnet algorithm we got 92% of Accuracy

Deep Learning for Multi-Label Facial Attribute Classification on Large-Scale Image Datasets (CelebA)

The exponential growth of data in recent years has led to an increasing demand for advanced techniques, especially those that work on large and complex data. This has given deep learning a significant advance in dealing with the tasks of analyzing, improving, and distinguishing big data. Our research focused on CNNs from this data and applying deep learning algorithms and their analysis to a large-scale image dataset. More specifically, our research focused on a dataset called CelebA, which contains more than 200,000 face images annotated with 40 binary facial features. It is a multi-label classification model based on the ResNet-50 architecture that has been fine-tuned to predict different facial features and hair color such as age, gender, and facial expressions. It was also trained using data augmentation, taking into account pose differences and background clutter to reduce imbalance between classes. These results reflect very strong predictive performance, with an average mean accuracy of 0.86 and an overall F1 score of 0.81 across all features. Attributes identified by clear visual cues—for example, “smiling,” “male ”and“ wearing lipstick”—were highly accurate, while less obvious attributes such as “big lips” and “narrow eyes” were more difficult to classify. We would like to point out that the results demonstrate the high efficiency of using deep learning models for multi-label classification on big data while solving problems associated with class imbalance and overfitting models. This research leads to the larger general field of big data analytics; in particular, it demonstrates how deep learning can be efficiently applied to large image datasets for automatic attribute recognition. It also opens up potential applications in areas such as biometric identification, surveillance, and human-computer interaction.

Enhancing Epstein–Barr virus detection in IBD patients with XAI and clinical data integration

BackgroundThere is increasing evidence of a link between Epstein–Barr virus (EBV) and gastrointestinal cancers, particularly in immunocompromised patients and those on thiopurines. Traditional endoscopic techniques have limitations in diagnosing EBV, whereas standard methods for diagnosing EBV have practical challenges in regular clinical settings related to their invasiveness and high costs. MethodsAn explainable AI (XAI) model was developed to analyze 1598 endoscopic images from 287 patients with inflammatory bowel disease (IBD), with a focus on detecting EBV infection. Following the application of data augmentation and transfer learning techniques, the model accurately classified the presence of EBV, and its performance was quantified through receiver operating characteristic (ROC) curve analysis, including calculation of the area under the curve (AUC). Furthermore, a combination of the Grad-CAM method and clinical data analysis significantly improved the interpretability and diagnostic accuracy of the model. ResultsOur model significantly outperformed other models, including those developed with ResNet50, Vision Transformer (ViT), and MobileNet v2 architectures, in detecting EBV on endoscopic images, achieving an accuracy of 73.83 % and an F1-score of 73.70 %. The model showed good performance in distinguishing between EBV-positive and EBV-negative images according to the confusion matrix and ROC curve analysis, with metrics including a true negative rate of 79.76 %, a true positive rate of 67.32 %, and an AUC of 0.74. Additionally, the generated salience maps effectively identified key regions in the images, enhancing lesion detection in patients with EBV infection. Our correlational analysis revealed significant associations between EBV infection and clinical parameters such as age, illness duration, and total bilirubin, and suggested that EBV infection had a notably greater incidence in ulcerative colitis (UC) patients than in Crohn's disease (CD) patients. ConclusionOur study successfully created an XAI-assisted system that allows the accurate detection of EBV in endoscopic images and improves the diagnosis of EBV infection through the integration of clinical data.

AI-enhanced interface for colonic polyp segmentation using DeepLabv3+ with comparative backbone analysis

Polyps are one of the early stages of colon cancer. The detection of polyps by segmentation and their removal by surgical intervention is of great importance for making treatment decisions. Although the detection of polyps through colonoscopy images can lead to multiple expert needs and time losses, it can also include human error. Therefore, automatic, fast, and highly accurate segmentation of polyps from colonoscopy images is important. Many methods have been proposed, including deep learning-based approaches. In this study, a method using DeepLabv3+ with encoder-decoder structure and ResNet architecture as backbone network is proposed for the segmentation of colonic polyps. The Kvasir-SEG polyp dataset was used to train and test the proposed method. After images were preprocessed, the training of the proposed network was performed. The trained network was then tested and performance metrics were calculated, and additionally, a GUI (Graphical User Interface) was designed to enable the segmentation of colonoscopy images for polyp segmentation. The experimental results showed that the ResNet-50 based DeepLabv3+ model had high performance metrics such as DSC: 0.9609, mIoU: 0.9246, demonstrating its effectiveness in the segmentation of colonic polyps. In conclusion, our method utilizing DeepLabv3+ with a ResNet-50 backbone achieves highly accurate colonic polyp segmentation. The obtained results demonstrate its potential to significantly enhance colorectal cancer diagnosis and planning for polypectomy surgery through automated image analysis.&#xD.

Color-based Classification of Dried Cocoa Beans from Various Origins of Indonesia by Image Analysis Using AlexNet and ResNet Architecture-Convolutional Neural Networks

Cocoa plant is widely cultivated in Indonesia and spread across various regions. Diversity in geographical conditions has been known to significantly affect the quality of cocoa beans. Practically, cocoa beans are often mixed without considering the variation in the quality and its origin. This resulted in reduced global quality and product inconsistency. Improved recognition and classification methods are needed to solve those problems. Non-destructive classification methods can be used to provide a more efficient classification process. The use of artificial intelligence with computer-based deep learning methods was used in this study. Beans samples of various origins (Aceh, Bali, Banten, Yogyakarta, East Kalimantan, West Sulawesi, and West Sumatera) were evaluated. From thecollected samples, 9100 images were then taken for data processing. Data preprocessing included denoising of the background image, cropping, resizing andchanging the storage extension through the training-validation stage and the testing process. AlexNet and ResNet architectures on a Convolutional NeuralNetwork were used for classification. The results showed that the average accuracy of cocoa image classification based on color identification by computer machines using Alexnet and ResNet was high (99.91% and 99.99%, respectively). This method can be applied to provide more efficient color-based cocoa bean classification for industrial purposes.

Comparison of Deep Learning and Yolov8 Models for Fox Detection Around the Henhouse

Human beings, who have been engaged in agriculture and animal husbandry for centuries, have to constantly track, take care and maintain their own agricultural lands and animals. This requires constant labor and time. The aim and originality of this study is to identify foxes that kidnap animals such as chickens, geese, ducks and turkeys that live in the coops of individuals engaged in poultry farming. In this way, even if the farmer is not in the henhouse at that moment, material and moral losses to the farmers will be prevented. To achieve this purpose, many images were collected to form dataset. The collected dataset was classified according to whether the fox was in the henhouse or not. Then, the outputs of DenseNet, MobileNet, ResNet50, VGG16, VGG19, Xception and Yolov8 architectures were fine tuned to be performed in transfer learning to detect existence of a fox in the henhouse. Then, the models were trained, and their performances were compared in terms of performance metrics such as loss, accuracy, precision and F1. In the results, Yolov8 architectures generally have demonstrated the best performances.

Pioneering automation in agricultural subsidy processing through deep learning for computer vision

ABSTRACT Validating farmers’ eligibility for subsidies under the European Union’s Common Agricultural Policy (CAP) requires substantial resources. An automated solution can minimize human errors and save time. This study leverages deep learning to automate the validation process using ground-level landscape images (GLLP), an approach not previously explored. We conducted two rigorous experiments to identify the optimal model architecture and assess the impact of data cleaning, accurate labelling, reduced feature ambiguity, and stricter photo collection protocols on performance. Our results reveal that while the ResNet-18 architecture achieved a recall of 0.61 on the original dataset, distinguishing between eligible and ineligible grasslands was challenging. By addressing image quality and enforcing standardized data collection, accuracy improved to 0.89 and recall to 0.94. Crucially, our research emphasizes the development and validation of effective data collection strategies, ensuring uniformity and high-quality data essential for accurate model performance. Our findings affirm the potential of deep learning to automate subsidy eligibility classification, contingent on resolving challenges related to the quality and standardization of GLLP data.

Primary angle-closed diseases recognition through artificial intelligence-based anterior segment-optical coherence tomography imaging.

In this study, artificial intelligence (AI) was used to deeply learn the classification of the anterior segment-Optical Coherence Tomography (AS-OCT) images. This AI systems automatically analyzed the angular structure of the AS-OCT images and automatically classified anterior chamber angle. It would improve the efficiency of AS-OCT image analysis. The subjects were from the glaucoma disease screening and prevention project for elderly people in Shanghai community. Each scan contained 72 cross-sectional AS-OCT frames. We developed a deep learning-based AS-OCT image automatic anterior chamber angle analysis software. Classifier performance was evaluated against glaucoma experts' grading of AS-OCT images as standard. Outcome evaluation included accuracy (ACC) and area under the receiver operator curve (AUC). 94895 AS-OCT images were collected from 687 participants, in which 69,243 images were annotated as open, 16,433 images were annotated as closed, and 9219 images were annotated as non-gradable. The class-balanced train data were formed from randomly extracting the same number of open angle images as the closed angle images, which contained 22,393 images (11127 open, 11256 closed). The best-performing classifier was developed by applying transfer learning to the ResNet-50 architecture. against experts' grading, this classifier achieved an AUC of 0.9635. Deep learning classifiers effectively detect angle closure based on automated analysis of AS-OCT images. This system could be used to automate clinical evaluations of the anterior chamber angle and improve efficiency of interpreting AS-OCT images. The results demonstrated the potential of the deep learning system for rapid recognition of high-risk populations of PACD.

A Fast Coding Unit Partitioning Decision Algorithm for Versatile Video Coding Based on Gradient Feedback Hierarchical Convolutional Neural Network and Light Gradient Boosting Machine Decision Tree

Video encoding technology is a foundational component in the advancement of modern technological applications. The latest standard in universal video coding, H.266/VVC, features a quad-tree with nested multi-type tree (QTMT) partitioning structure, which represents an improvement over its predecessor, High-Efficiency Video Coding (H.265/HEVC). This configuration facilitates adaptable block segmentation, albeit at the cost of heightened encoding complexity. In view of the aforementioned considerations, this paper puts forth a deep learning-based approach to facilitate CU partitioning, with the aim of supplanting the intricate CU partitioning process observed in the Versatile Video Coding Test Model (VTM). We begin by presenting the Gradient Feedback Hierarchical CNN (GFH-CNN) model, an advanced convolutional neural network derived from the ResNet architecture, enabling the extraction of features from 64 × 64 coding unit (CU) blocks. Following this, a hierarchical network diagram (HND) is crafted to depict the delineation of partition boundaries corresponding to the various levels of the CU block’s layered structure. This diagram maps the features extracted by the GFH-CNN model to the partitioning at each level and boundary. In conclusion, a LightGBM-based decision tree classification model (L-DT) is constructed to predict the corresponding partition structure based on the prediction vector output from the GFH-CNN model. Subsequently, any errors in the partitioning results are corrected in accordance with the encoding constraints specified by the VTM, which ultimately determines the final CU block partitioning. The experimental results demonstrate that, in comparison with VTM-10.0, the proposed algorithm achieves a 48.14% reduction in complexity with only a 0.83% increase in bitrate under the top-three configuration, which is negligible. In comparison, the top-two configuration resulted in a higher complexity reduction of 63.78%, although this was accompanied by a 2.08% increase in bitrate. These results demonstrate that, in comparison to existing solutions, our approach provides an optimal balance between encoding efficiency and computational complexity.

Deep learning in the advanced core sample porosity determination with XCT image

X-ray computed tomography (XCT) for core sample porosity analysis has advanced significantly, integrating machine learning with traditional image processing and introducing methods such as Weighted Assignment by CT number calculation. However, several areas still require enhancement, particularly in image preprocessing and model optimization for specific deep learning approaches. Moreover, the application of the Weighted Assignment method to water-sensitive mudstone core samples in this study revealed the inconsistency with the porosity estimation, primarily due to the overemphasis on fracture effects. To address these limitations, this study proposed the ResNet algorithm, a deep learning model developed for XCT-based porosity estimation. ResNet's architecture, incorporating residual learning with shortcut connections, enables effective training of deep networks and enhances feature extraction. The ResNet-based model was trained on XCT images with known porosity values measured by Multi-Sensor Core Logging (MSCL), effectively predicting volume fraction porosity for materials with similar lithological characteristics. Analysis of testing losses, comparison of predicted versus actual porosity values and RRMSE, demonstrates the model's superior performance than traditional methods. This novel application of ResNet to porosity estimation highlights the potential of deep learning to significantly enhance accuracy by 97.82% compared with the weighted assignment method with RRMSE.

A self-supervised deep-driven model for automatic weather classification from remote sensing images

ABSTRACT The increasing use of Remote Sensing (RS) technology has resulted in the availability of large volumes of satellite imaging data, necessitating efficient, and scalable solutions for rapid analysis and classification in various interdisciplinary fields. This research focuses on addressing these challenges by examining different training and optimization techniques to achieve the highest classification accuracy on both labelled and unlabelled data from the Large-Scale Cloud Photos Dataset for Meteorology Research (LSCIDMR), which contains 10 high-resolution classes. A key aspect of this study is the implementation of a unique deep learning model based on the ResNet101 architecture, designed to handle the inter-class similarity problem through architectural improvements and imbalanced class distributions using data augmentation techniques. Furthermore, this model integrates a novel approach that combines self-learning with semi-supervised learning to process large amounts of unlabelled data. An iterative self-learning process is employed for continuous refinement, using pseudo-labels generated from the unlabelled data. To ensure higher quality and relevance, confidence-based selection of pseudo-labels is applied. Our model demonstrates superior performance in classification compared to recently reported deep learning algorithms, offering a robust solution for the automated categorization of satellite images. In this study, our ResNet101 model achieved a mean accuracy of 0.917 (±0.02), a mean precision of 0.917 (±0.01), with a mean F1 score of 0.9300 (±0.015) over five folds.

Leveraging Custom CNN and ResNet Architectures for Identifying Deviant Personality Traits in Cyber Threat Detection Using the Five Factor Model

Leveraging Custom CNN and ResNet Architectures for Identifying Deviant Personality Traits in Cyber Threat Detection Using the Five Factor Model

Real-Time On-Site Ground-Motion Prediction Using ResNet

Abstract On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023.

Improved Osteoporosis Prediction in Breast Cancer Patients Using a Novel Semi-Foundational Model.

Small cohorts of certain disease states are common especially in medical imaging. Despite the growing culture of data sharing, information safety often precludes open sharing of these datasets for creating generalizable machine learning models. To overcome this barrier and maintain proper health information protection, foundational models are rapidly evolving to provide deep learning solutions that have been pretrained on the native feature spaces of the data. Although this has been optimized in Large Language Models (LLMs), there is still a sparsity of foundational models for computer vision tasks. It is in this space that we provide an investigation into pretraining Visual Geometry Group (VGG)-16, Residual Network (ResNet)-50, and Dense Network (DenseNet)-121 on an unrelated dataset of 8500 chest CTs which was subsequently fine-tuned to classify bone mineral density (BMD) in 199 breast cancer patients using the L1 vertebra on CT. These semi-foundational models showed significant improved ternary classification into mild, moderate, and severe demineralization in comparison to ground truth Hounsfield Unit (HU) measurements in trabecular bone with the semi-foundational ResNet50 architecture demonstrating the best relative performance. Specifically, the holdout testing AUC was 0.99 (p-value < 0.05, ANOVA versus no pretraining versus ImageNet transfer learning) and F1-score 0.99 (p-value < 0.05) for the holdout testing set. In this study, the use of a semi-foundational model trained on the native feature space of CT provided improved classification in a completely disparate disease state with different window levels. Future implementation with these models may provide better generalization despite smaller numbers of a disease state to be classified.

AttentionEP: Predicting Essential Proteins via Fusion of Multiscale Features by Attention Mechanisms

Identifying essential proteins is of utmost importance in the field of biomedical research due to their essential functions in cellular activities and their involvement in mechanisms related to diseases. In this research, a novel approach called AttentionEP for predicting essential proteins (EP) is introduced by attention mechanisms. This method leverages both cross-attention and self-attention frameworks, focusing on enhancing prediction accuracy through the integration of features across diverse scales. Spatial characteristics of proteins are obtained from the protein-protein interaction (PPI) network by employing Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT). Following this, Bidirectional Long Short-Term Memory networks (BiLSTM) are employed to derive temporal features from gene expression datasets. Furthermore, spatial characteristics are derived by integrating data on subcellular localization with the application of Deep Neural Networks (DNN). In order to effectively integrate features across multiple scales, initial steps involve the application of self-attention techniques to derive essential insights from each unique data set. Following this, mechanisms involving self-attention and cross-attention are employed to enhance the interaction between diverse information sources. To identify essential proteins, a classifier based on the ResNet architecture is developed. The findings from the experiments indicate that the method introduced here shows superior performance in identifying essential proteins, recording an Area Under the Curve (AUC) value of 0.9433. This approach shows a considerable advantage over established techniques. The findings of this study provide a significant advancement in the comprehension of critical proteins, revealing promising potential for applications in the development of therapeutics and addressing various diseases.

Exploring Fish Species Exploring Fish Species Classification using Deep Learning

Fishes are intriguing creatures that live in a wide range of aquatic settings, including freshwater rivers and lakes, oceans, and deep-sea trenches. Freshwater fish are considered a poor man's protein supplement since they are readily available in lakes, rivers, natural ponds, rice fields, beels, and fisheries. Many freshwater fish species resemble one another, making it difficult to classify them based on their exterior appearance. Manual fish species identification is always error-prone since it requires knowledge. Recently, computer vision and deep learning have played a key role in underwater species classification and detection studies. This research focuses on fish species categorization with Convolutional Neural Networks (CNNs) and two popular architectures, ResNet50 and VGG16. The goal of this research is to create an automated system that can correctly recognize and classify different fish species based on dataset. The two architecture ResNet50 and VGG16 are fine-tuned and CNN for training and validation of the collected fish data. The suggested method entails preprocessing the image data, training the CNN models with ResNet50 and VGG16 architectures, and assessing their performance using measures such as accuracy, precision, recall, and F1 score. This study reports the best overall classification accuracy, precision, and recall rate, which is almost 95%. Comprehensive empirical investigation has proven that when the weight and bias learning rate increases, so does the classifier's validation loss.

Intestinal Polyp Segmentation Based on Histogram Equalization ResNet (PE-ResNet)

Colonoscopy is an important technical means for screening early colorectal cancer lesions. Accurate segmentation of intestinal polyps helps improve the accuracy of screening. Early screening for lesions is of great significance for the prevention of colorectal cancer, and the segmentation of intestinal polyps is an important research direction. Although intestinal polyp segmentation based on deep learning has achieved acceptable performance, the color variation among intestinal endoscopic images significantly affects it. Based on the ResNet architecture, this study proposes an advanced PE-ResNet in which histogram equalization is used to reduce color influence. Experimental results on five datasets, including ClinicDB, demonstrate that the PE-ResNet model achieves improved performance in intestinal polyp segmentation.