Classification Of Lesions Research Articles

Analysis of efficacy of picosecond laser treatment for nevus of Ota.

To analyze the efficacy, safety, and influencing factors of 1064nm picosecond Nd: YAG laser(PSAL)treatment for Nevus of Ota. From January 2018 to March 2024, 212 patients with Nevus of Ota were treated with 1064nm picosecond Nd: YAG laser at the Guangzhou Dermatology Hospital. The study analyzed the impact of patient gender, age, Tanino classification of skin lesions, color of skin lesions, Fitzpatrick classification of skin, and number of treatments on efficacy, while also documenting any adverse reactions to the treatment. Overall, out of the 212 patients who underwent 1 to 11 treatment sessions, 80 cases (37.73%) achieved complete clearance, 42 cases (19.81%) showed excellent outcomes, 45 cases (21.22%) had good outcomes, 37 cases (17.45%) saw fair outcomes, and 8 cases (3.77%) had minimal effect, resulting in an overall effective rate of 78.77%. The study revealed a correlation between treatment efficacy, age, and the number of laser treatments (P < 0.05). Multifactorial logistic regression analysis demonstrated a positive association with treatment effectiveness (odds ratio 3.324; 95% confidence interval 2.110-5.238). Other variables such as gender, Tanino classification of skin lesions, color of skin lesions, and Fitzpatrick skin type showed no significant correlation with efficacy (P > 0.05). Among the patients, two experienced temporary post-inflammatory effects, while three exhibited folliculitis. Notably, there were no reported cases of persistent erythema, scarring, or hypopigmentation. The treatment of Nevus of Ota using 1064nm picosecond Nd: YAG laser has been demonstrated to be both safe and effective, with treatment efficacy impacted by patient age and the number of treatment sessions. Early intervention and higher frequency of treatment sessions are associated with improved treatment outcomes.

Training State-of-the-Art Deep Learning Algorithms with Visible and Extended Near-Infrared Multispectral Images of Skin Lesions for the Improvement of Skin Cancer Diagnosis

An estimated 60,000 people die annually from skin cancer, predominantly melanoma. The diagnosis of skin lesions primarily relies on visual inspection, but around half of lesions pose diagnostic challenges, often necessitating a biopsy. Non-invasive detection methods like Computer-Aided Diagnosis (CAD) using Deep Learning (DL) are becoming more prominent. This study focuses on the use of multispectral (MS) imaging to improve skin lesion classification of DL models. We trained two convolutional neural networks (CNNs)—a simple CNN with six two-dimensional (2D) convolutional layers and a custom VGG-16 model with three-dimensional (3D) convolutional layers—using a dataset of MS images. The dataset included spectral cubes from 327 nevi, 112 melanomas, and 70 basal cell carcinomas (BCCs). We compared the performance of the CNNs trained with full spectral cubes versus using only three spectral bands closest to RGB wavelengths. The custom VGG-16 model achieved a classification accuracy of 71% with full spectral cubes and 45% with RGB-simulated images. The simple CNN achieved an accuracy of 83% with full spectral cubes and 36% with RGB-simulated images, demonstrating the added value of spectral information. These results confirm that MS imaging provides complementary information beyond traditional RGB images, contributing to improved classification performance. Although the dataset size remains a limitation, the findings indicate that MS imaging has significant potential for enhancing skin lesion diagnosis, paving the way for further advancements as larger datasets become available.

A Deep Learning with Metaheuristic Optimization-Driven Breast Cancer Segmentation and Classification Model using Mammogram Imaging

Cancer is the second leading cause of death globally, with Breast Cancer (BC) accounting for 20% of the new diagnoses, making it a major cause of morbidity and mortality. Mammography is effective for BC detection, but lesion interpretation is challenging, prompting the development of Computer-Aided Diagnosis (CAD) systems to assist in lesion classification and detection. Machine Learning (ML) and Deep Learning (DL) models are widely used in disease diagnosis. Therefore, this study presents an Optimized Graph Convolutional Recurrent Neural Network based Segmentation for Breast Cancer Recognition and Classification (OGCRNN-SBCRC) technique. In the preparation phase, images and masks are annotated and then classified as benign or malignant. To achieve this, the Wiener Filter (WF)-based noise removal and log transform-based contrast enhancement are used for preprocessing. The OGCRNN-SBCRC technique utilizes the UNet++ method for segmentation and the RMSProp optimizer for parameter tuning. In addition, the OGCRNN-SBCRC technique employs the ConvNeXtTiny Convolution Neural Network (CNN) approach for feature extraction. For BC classification and detection, the Graph Convolutional Recurrent Neural Network (GCRNN) model is used. Finally, the Aquila Optimizer (AO) model is employed for the hyperparameter tuning of the GCRNN approach. The simulation analysis of the OGCRNN-SBCRC methodology, using the BC image dataset, demonstrated superior performance with an accuracy of 99.65%, surpassing existing models.

Accuracy of a Cascade Network for Semi-Supervised Maxillary Sinus Detection and Sinus Cyst Classification.

Maxillary sinus mucosal cysts represent prevalent oral and maxillofacial diseases, and their precise diagnosis is essential for surgical planning in maxillary sinus floor elevation. This study aimed to develop a deep learning-based pipeline for the classification of maxillary sinus lesions in cone beam computed tomography (CBCT) images to provide auxiliary support for clinical diagnosis. This study utilized 45 136 maxillary sinus images from CBCT scans of 541 patients. A cascade network was designed, comprising a semi-supervised maxillary sinus area object detection module and a maxillary sinus lesions classification module. The object detection module employed a semi-supervised pseudo-labelling training strategy to expand the maxillary sinus annotation dataset. In the classification module, the performance of Convolutional Neural Network and Transformer architectures was compared for maxillary sinus mucosal lesion classification. The object detection and classification modules were evaluated using metrics including Accuracy, Precision, Recall, F1 score, and Average Precision, with the object detection module additionally assessed using Precision-Recall Curve. The fully supervised pseudo-label generation model achieved an average accuracy of 0.9433, while the semi-supervised maxillary sinus detection model attained 0.9403. ResNet-50 outperformed in classification, with accuracies of 0.9836 (sagittal) and 0.9797 (coronal). Grad-CAM visualization confirmed accurate focus on clinically relevant lesion features. The proposed pipeline achieves high-precision detection and classification of maxillary sinus mucosal lesions, reducing manual annotation while maintaining accuracy.

Melanoma Detection Using Deep Learning Techniques

Skin Cancer, particularly melanoma, is one of the most prevalent and deadly forms of cancer globally. Early detection significantly improves survival rates, but manual diagnosis by dermatologists is often subjective and time-consuming. This study proposes a deep learning-based approach to skin cancer prediction, focusing on the classification of malignant and benign lesions. Leveraging a convolutional neural network (CNN) architecture, the model is trained on a large dataset of dermoscopic images. The system automates feature extraction, reducing human bias and error. Our model achieves state-of-the-art performance by incorporating techniques such as data aug- mentation, transfer learning, and fine-tuning on pre-trained networks (such as ResNet &amp; VGG ), which improve the model’s ability to generalize across diverse skin tones and lesion types. Evaluation metrics such as accuracy, sensitivity, specificity, and AUC (Area Under the Curve) are used to assess the model’s efficacy. The results demonstrate the potential of deep learning to en- hance early diagnosis, aiding dermatologists in clinical decision-making and providing a foundation for scalable, accessible skin cancer screening tools. Keywords: Skin Cancer, Deep Learning, CNN, Early Detection, Healthcare AI

Class-Wise Combination of Mixture-Based Data Augmentation for Class Imbalance Learning of Focal Liver Lesions in Abdominal CT Images.

In this paper, we propose a method to address the class imbalance learning in the classification of focal liver lesions (FLLs) from abdominal CT images. Class imbalance is a significant challenge in medical image analysis, making it difficult for machine learning models to learn to classify them accurately. To overcome this, we propose a class-wise combination of mixture-based data augmentation (CCDA) method that uses two mixture-based data augmentation techniques, MixUp and AugMix. These are applied at different ratios for each class to adaptively learn features for each class. This method is tailored to handle the unique characteristics of each FLL type by adjusting the augmentation mix for major classes (e.g., cysts and metastases) and minor classes (e.g., hemangiomas). In experiments, our method was validated on the dataset consisted of portal phase CT images from 1290 colorectal cancer patients. The results showed that applying MixUp and AugMix in a class-wise manner could significantly improve the classification performance of minor classes while maintaining or slightly improving the performance of major classes. Quantitative results showed higher F1 scores for minor classes and a more balanced accuracy across classes when CCDA was used compared to other methods.

Integrating Explainable AI for Skin Lesion Classifications: A Systematic Literature Review

Skin cancer, particularly melanoma, poses a significant global health challenge due to its prevalence and mortality rate. Early detection is critical to improving outcomes, as advanced cases become increasingly difficult to treat. The advent of Artificial Intelligence (AI) and Explainable AI (XAI) techniques has revolutionized dermatological diagnostics by offering accurate and interpretable solutions. This systematic review investigates the integration of XAI in skin lesion classification, analyzing 22 recent studies published between 2019 and 2023. The studies encompass diverse approaches, including deep learning models like CNNs, ResNet, DenseNet, and MobileNet, as well as explainability techniques such as Grad-CAM, SHAP, and saliency maps. Results highlight significant advancements in accuracy and interpretability, with some models achieving over 99% accuracy on datasets like ISIC 2018 and HAM10000. However, challenges persist, including dataset imbalances, limited diversity in patient metadata, and generalizability across different skin types and imaging conditions. XAI methods, by visualizing model decision pathways, enhance transparency, fostering trust among clinicians and enabling seamless AI integration into clinical practice. This review underscores the potential of combining state-of-the-art AI models with explainable frameworks to address the complexities of skin lesion diagnostics. It advocates for future research to prioritize diverse, metadata-rich datasets, innovative optimization techniques, and robust architectures to develop reliable, interpretable diagnostic tools. By bridging the gap between advanced AI and user understanding, this work contributes to the creation of clinically applicable, trustable AI-driven healthcare solutions.

P0537 Prognostic significance of anastomotic lesions in post-operative Crohn’s disease

Abstract Background Endoscopic evaluation (assessed with the Rutgeerts score, RS) offers prognostic information in the postoperative management of Crohn’s disease (CD). The significance of anastomotic lesions (AL) remains unclear. Two scores have been introduced: the POCER index1 (PI), including circumferential extent and depth of anastomotic ulcers, and the UEDA et al score2 (US), classifying 4 categories of lesions at the anastomosis and peri-anastomosis site. This study aims to evaluate the predictive value of PI and the US in a real- life cohort. Methods We conducted a retrospective study including CD patients undergoing ileo-colonic resection. All patients underwent first endoscopic evaluation within 6-12 months to assess endoscopic recurrence (ER), measured with RS, PI and US. ER was defined as RS ≥ 2, PI ≥ 2 or US ≥ intermediate. Clinical recurrence (CR) was assessed at 18-24 months using the HBI. A second endoscopy at 18-24 months was available for a subgroup of patients. Results We included 177 patients, 75% of them with no previous history of surgery. All patients had underwent prophylactic therapy with biologics. At 6-12 months, ER was observed in 91 (51%), 35 (20%) and 51 (28%) of patients, according to RS, PI and US, respectively. CR was observed in 34 patients (18.6%). Among 98 pts (55%) with a second endoscopy, ER was present in 45 pts (45%). Looking the predictive value for CR, a RS ≥2 showed an AUROC of 0.74 (sensitivity 82.7%, specificity 56.9%); PI ≥2 an AUROC of 0.613 (sensitivity 31%, specificity 82.5%) and US ≥ intermediate an AUROC of 0.668 (sensitivity 50%, specificity 74.6%). About ER, a RS ≥2 showed an AUROC of 0.77 (sensitivity 70.9%, specificity 79.07%), a PI≥2 with an AUROC of 0.65 (sensitivity 38.18%, specificity 93.02%) and US an AUROC of 0.74 (sensitivity 49.06%, specificity 93.03%). We performed a subgroup analysis among 108 patients with exclusively AL. For CR, the RS&amp;gt;=2 showed an AUROC of 0.67 (sensitivity 58%, specificity 74%). A PI &amp;gt;=2 predicted CR with an AUROC of 0.66 (sensitivity of 25%, specificity of 92%), while the US with an AUROC of 0.63 (sensitivity of 16%, specificity of 93.4%). Regarding ER, RS&amp;gt;=2 showed an AUROC of 0.667 (sensitivity 45%, specificity 89%), PI an AUROC of 0.63 (sensitivity 20%, specificity 97%) and the US&amp;gt;= intermediate an AUROC of 0.65 (sensitivity 16%, specificity 100%). Conclusion Grading the anastomotic lesions with specific scores seems to improve the prognostic value of the early endoscopic assessment after resection surgery. References 1)De Cruz P, Hamilton AL, Burrell KJ, Gorelik A, Liew D, Kamm MA. Endoscopic Prediction of Crohn’s Disease Postoperative Recurrence. Inflamm Bowel Dis. 2022 May 4;28(5):680-688. doi: 10.1093/ibd/izab134. PMID: 34231852.2) 2)Ueda T, Koyama F, Sugita A, Ikeuchi H, Futami K, Fukushima K, Nezu R, Iijima H, Mizushima T, Itabashi M, Watanabe K, Hata K, Shinagawa T, Matsuoka K, Takenaka K, Sasaki M, Nagayama M, Yamamoto H, Shinozaki M, Fujiya M, Kato J, Ueno Y, Tanaka S, Okita Y, Hashimoto Y, Kobayashi T, Koganei K, Uchino M, Fujii H, Suzuki Y, Hisamatsu T. Endoscopic Lesions of Postoperative Anastomotic Area in Patients With Crohn’s Disease in the Biologic Era: A Japanese Multi-Centre Nationwide Cohort Study. J Crohns Colitis. 2023 Dec 30;17(12):1968-1979. doi: 10.1093/ecco-jcc/jjad116. PMID: 37450892.

Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis - a systematic review.

Magnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often complemented by lumbar puncture-a highly invasive method-to validate the diagnosis. Additionally, MRI is periodically repeated to monitor disease progression and treatment efficacy. Recent research has focused on the application of artificial intelligence (AI) and radiomics in medical image processing, diagnosis, and treatment planning. A review of the current literature was conducted, analyzing the use of AI models and texture analysis for MS lesion segmentation and classification. The study emphasizes common models, including U-Net, Support Vector Machine, Random Forest, and K-Nearest Neighbors, alongside their evaluation metrics. The analysis revealed a fragmented research landscape, with significant variation in model architectures and performance. Evaluation metrics such as Accuracy, Dice score, and Sensitivity are commonly employed, with some models demonstrating robustness across multi-center datasets. However, most studies lack validation in clinical scenarios. The absence of consensus on the optimal model for MS lesion segmentation highlights the need for standardized methodologies and clinical validation. Future research should prioritize clinical trials to establish the real-world applicability of AI-driven decision support tools. This review provides a comprehensive overview of contemporary advancements in AI and radiomics for analyzing and monitoring emerging MS lesions in MRI.

Deep learning-based MVIT-MLKA model for accurate classification of pancreatic lesions: a multicenter retrospective cohort study.

Accurate differentiation between benign and malignant pancreatic lesions is critical for effective patient management. This study aimed to develop and validate a novel deep learning network using baseline computed tomography (CT) images to predict the classification of pancreatic lesions. This retrospective study included 864 patients (422 men, 442 women) with confirmed histopathological results across three medical centers, forming a training cohort, internal testing cohort, and external validation cohort. A novel hybrid model, Multi-Scale Large Kernel Attention with Mobile Vision Transformer (MVIT-MLKA), was developed, integrating CNN and Transformer architectures to classify pancreatic lesions. The model's performance was compared with traditional machine learning methods and advanced deep learning models. We also evaluated the diagnostic accuracy of radiologists with and without the assistance of the optimal model. Model performance was assessed through discrimination, calibration, and clinical applicability. The MVIT-MLKA model demonstrated superior performance in classifying pancreatic lesions, achieving an AUC of 0.974 (95% CI 0.967-0.980) in the training set, 0.935 (95% CI 0.915-0.954) in the internal testing set, and 0.924 (95% CI 0.902-0.945) in the external validation set, outperforming traditional models and other deep learning models (P < 0.05). Radiologists aided by the MVIT-MLKA model showed significant improvements in diagnostic accuracy and sensitivity compared to those without model assistance (P < 0.05). Grad-CAM visualization enhanced model interpretability by effectively highlighting key lesion areas. The MVIT-MLKA model efficiently differentiates between benign and malignant pancreatic lesions, surpassing traditional methods and significantly improving radiologists' diagnostic performance. The integration of this advanced deep learning model into clinical practice has the potential to reduce diagnostic errors and optimize treatment strategies.

Bilinear Interpolation Augmented Deep Feature Extraction with an Improved Remora Optimization-Based Deep Convolutional Neural Network for Skin Lesion Classification

Bilinear Interpolation Augmented Deep Feature Extraction with an Improved Remora Optimization-Based Deep Convolutional Neural Network for Skin Lesion Classification

Hierarchical skin lesion image classification with prototypical decision tree

Traditional disease classification models often disregard the clinical significance of misclassifications and lack interpretability. To overcome these challenges, we propose a hierarchical prototypical decision tree (HPDT) for skin lesion classification. HPDT combines prototypical networks and decision trees, leveraging a class hierarchy to guide interpretable predictions from general to specific categories. By incorporating a hierarchy-based distance matrix, the model prioritizes less severe misclassifications while maintaining diagnostic accuracy. Evaluated on a dataset of 235,268 dermoscopic images across 65 conditions, HPDT outperforms flat classifiers and existing hierarchical methods in accuracy, error severity reduction, and interpretability. It also generalizes effectively to unseen classes. These results highlight the value of integrating clinical hierarchies into model design and training to improve diagnostic reliability and decision transparency, demonstrating HPDT’s potential for clinical decision support.

Skin Lesion Classification Through Test Time Augmentation and Explainable Artificial Intelligence.

Despite significant advancements in the automatic classification of skin lesions using artificial intelligence (AI) algorithms, skepticism among physicians persists. This reluctance is primarily due to the lack of transparency and explainability inherent in these models, which hinders their widespread acceptance in clinical settings. The primary objective of this study is to develop a highly accurate AI-based algorithm for skin lesion classification that also provides visual explanations to foster trust and confidence in these novel diagnostic tools. By improving transparency, the study seeks to contribute to earlier and more reliable diagnoses. Additionally, the research investigates the impact of Test Time Augmentation (TTA) on the performance of six Convolutional Neural Network (CNN) architectures, which include models from the EfficientNet, ResNet (Residual Network), and ResNeXt (an enhanced variant of ResNet) families. To improve the interpretability of the models' decision-making processes, techniques such as t-distributed Stochastic Neighbor Embedding (t-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) are employed. t-SNE is utilized to visualize the high-dimensional latent features of the CNNs in a two-dimensional space, providing insights into how the models group different skin lesion classes. Grad-CAM is used to generate heatmaps that highlight the regions of input images that influence the model's predictions. Our findings reveal that Test Time Augmentation enhances the balanced multi-class accuracy of CNN models by up to 0.3%, achieving a balanced accuracy rate of 97.58% on the International Skin Imaging Collaboration (ISIC 2019) dataset. This performance is comparable to, or marginally better than, more complex approaches such as Vision Transformers (ViTs), demonstrating the efficacy of our methodology.

Association of patellofemoral osteoarthritis on patient-reported outcomes after medial unicompartmental knee arthroplasty: a retrospective cohort study.

In contemporary medial unicompartmental knee arthroplasty (mUKA), non-lateral patellofemoral osteoarthritis (PFOA) is not considered a contraindication. However, we still lack knowledge on the association of PFOA severity on patient reported outcome measures (PROMs) after mUKA. We aimed to examine the association between PFOA severity and PROM-score changes after mUKA. We included 549 mobile-bearing mUKAs. PFOA was graded intraoperatively as 0 = normal cartilage, 1-2 = superficial changes or < 50% of depth, and 3-4 = changes of > 50% of depth or to the bone, using the International Cartilage Repair Society (ICRS) cartilage lesion classification system. All patients completed the Oxford Knee Score (OKS), Activity and Participation Questionnaire (APQ), and Forgotten Joint Score (FJS), preoperatively and 3, 12, and 24 months postoperatively. PROM changes were compared using linear regression models adjusted for sex, age, body mass index, and preoperative PROM score. We found no significant differences in OKS, FJS, and APQ change when comparing group 3-4 with group 0 at any follow-up. When comparing group 1-2 with 0 we found a statistical but not clinical significantly higher change in OKS scores at 24-month follow-up (2.5, 95% confidence interval [CI] 0.36-4.6) and in APQ scores at 24-month follow-up (10.6, CI 1.2-20.0) in favor of group 1-2. Severe PFOA, excluding severe lateral facet PFOA, had no negative association on PROM score development following mobile-bearing mUKA.

In vivo Predictors of Focal Type In-stent Restenosis: A Clinical, Angiographical and Optical Coherence Tomography Study.

Introduction：Few studies have evaluated different patterns of in-stent restenosis by optical coherence tomography (OCT). This study aims to identify in vivo predictors for focal restenosis in patients with in-stent restenosis (ISR). Methods: The study recruited patients with ISR who underwent OCT examination in the Cardiology Department of the Affiliated Hospital of Zunyi Medical University from October 2018 to December 2022. Based on the angiographic classification of ISR lesions, the patients were divided into two groups: the focal group (n=58) and the non-focal group (n=158). Results： The white blood cell count was higher in the non-focal group than those in focal type (7.8±3.0 vs. 6.6±2.1, P = 0.007). The prevalence of lipid rich plaque was higher in patients with focal ISR (65.5% vs. 42.4%, P = 0.003). The occurrence of red thrombus (27.8% vs. 12.1%, P = 0.016) and white thrombus (41.1% vs. 24.1%, P = 0.021) was higher in the non-focal group. Multivariate analysis showed that low density lipoprotein cholesterol C (odds ratio [OR]:3.341, 95% confidence interval [CI]: 1.714-9.784, P = 0.046) was independently associated with focal restenosis. While white blood cell count (OR: 0.814, 95% CI: 0.657-0.913, P = 0.047) and stent malapposition (OR: 0.228, 95% CI: 0.057-0.896, P = 0.037) were independently associated with non-focal restenosis. Conclusion：There were significant differences in clinical baselines and OCT identified morphological characteristics in patients between focal and non-focal group. Low density lipoprotein cholesterol C was independent associated with focal restenosis. White blood cell count and stent malapposition were correlated with non-focal restenosis.

Characterization of hepatocellular carcinomawith CT with deep learning reconstruction compared with iterative reconstruction and 3-Tesla MRI.

This study compared the characteristics of lesions suspicious for hepatocellular carcinoma (HCC) and their LI-RADS classifications in adaptive statistical iterative reconstruction (ASIR) and deep learning reconstruction (DLR) to those of MR images, along with radiologist confidence. This prospective single-center trial included patients who underwent four-phase liver CT and multiphasic contrast-enhanced MRI within 7 days from February to August 2023. The lesion characteristics, LI-RADS classifications and confidence scores according to two radiologists on the ASIR, DLR and MRI techniques were compared. If the patient had at least one lesion, he was included in the HCC group, otherwise in the non-HCC group. MRI being the technique with the best sensitivity, concordance of lesions characteristics and LI-RADS classifications were calculated by weighted kappa between the ASIR and MRI and between the DLR and MRI. The confidence scores are expressed as the means and standard deviations. Eighty-nine patients were enrolled, 52 in the HCC group (67 years ± 9 [mean ± SD], 46 men) and 37 in the non-HCC group (68 years ± 9, 33 men). The concordance coefficient between the LI-RADS classification by ASIR and MRI was 0.64 [0.52; 0.76], showing good agreement, that by DLR and MRI was 0.83 [0.73; 0.92], showing excellent agreement. The diagnostic confidence in ASIR was 3.31 ± 0.95 (mean ± SD) and 3.0 ± 1.11, that in the DLR was 3.9 ± 0.88 and 4.11 ± 0.75, that in the MRI was 4.46 ± 0.80 and 4.57 ± 0.80. DLR provided excellent LI-RADS classification concordance with MRI, whereas ASIR provided good concordance. The radiologists' confidence was greater in the DLR than in the ASIR but remained highest in the MR group. Question Does the use of deep learning reconstructions (DLR) improve LI-RADS classification of suspicious hepatocellular carcinoma lesions compared to adaptive statistical iterative reconstructions (ASIR)? Findings DLR demonstrated superior concordance of LI-RADS classification with MRI compared to ASIR. It also provided greater diagnostic confidence than ASIR. Clinical relevance The use of DLR enhances radiologists' ability to visualize and characterize lesions suspected of being HCC, as well as their LI-RADS classification. Moreover, it also boosts their confidence in interpreting these images.

SL-R-CNN-HHO: multi-class skin lesion classification using region-based convolutional neural networks and harris hawk optimization on the HAM dataset

SL-R-CNN-HHO: multi-class skin lesion classification using region-based convolutional neural networks and harris hawk optimization on the HAM dataset

Lesion classification and diabetic retinopathy grading by integrating softmax and pooling operators into vision transformer.

Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is labor-intensive, time-consuming, and error-prone. Therefore, plenty of automated screening technique have been developed to address this task. Among these techniques, the deep learning models have demonstrated promising outcomes in various types of machine vision tasks. However, most of the medical image analysis-oriented deep learning approaches are built upon the convolutional operations, which might neglect the global dependencies between long-range pixels in the medical images. Therefore, the vision transformer models, which can unveil the associations between global pixels, have been gradually employed in medical image analysis. However, the quadratic computation complexity of attention mechanism has hindered the deployment of vision transformer in clinical practices. Bearing the analysis above in mind, this study introduces an integrated self-attention mechanism with both softmax and linear modules to guarantee efficiency and expressiveness, simultaneously. To be specific, a portion of query and key tokens, which are much less than the original query and key tokens, are adopted in the attention module by adding a set of proxy tokens. Note that the proxy tokens can fully utilize both the advantages of softmax and linear attention. To evaluate the performance of the presented approach, the comparison experiments between state-of-the-art algorithms and the proposed approach are conducted. Experimental results demonstrate that the proposed approach achieves superior outcome over the state-of-the-art algorithms on the publicly available datasets. Accordingly, the proposed approach can be taken as a potentially valuable instrument in clinical practices.

Label credibility correction based on cell morphological differences for cervical cells classification

Cervical cancer is one of the deadliest cancers that pose a significant threat to women’s health. Early detection and treatment are commonly used methods to prevent cervical cancer. The use of pathological image analysis techniques for the automatic interpretation of cervical cells in pathological slides is a prominent area of research in the field of digital medicine. According to The Bethesda System, cervical cytology necessitates further classification of precancerous lesions based on positive interpretations. However, clinical definitions among different categories of lesion are complex and often characterized by fuzzy boundaries. In addition, pathologists can deduce different criteria for judgment based on The Bethesda System, leading to potential confusion during data labeling. Noisy labels due to this reason are a great challenge for supervised learning. To address the problem caused by noisy labels, we propose a method based on label credibility correction for cervical cell images classification network. Firstly, a contrastive learning network is used to extract discriminative features from cell images to obtain more similar intra-class sample features. Subsequently, these features are fed into an unsupervised method for clustering, resulting in unsupervised class labels. Then unsupervised labels are corresponded to the true labels to separate confusable and typical samples. Through a similarity comparison between the cluster samples and the statistical feature centers of each class, the label credibility analysis is carried out to group labels. Finally, a cervical cell images multi-class network is trained using synergistic grouping method. In order to enhance the stability of the classification model, momentum is incorporated into the synergistic grouping loss. Experimental validation is conducted on a dataset comprising approximately 60,000 cells from multiple hospitals, showcasing the effectiveness of our proposed approach. The method achieves 2-class task accuracy of 0.9241 and 5-class task accuracy of 0.8598. Our proposed method achieves better performance than existing classification networks on cervical cancer.

Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification.

Skin cancer is considered globally as the most fatal disease. Most likely all the patients who received wrong diagnosis and low-quality treatment die early. Though if it is detected in the early stages the patient has fairly good chance and the aforementioned diseases can be cured. Consequently, diagnostic identification and management of the patient at this level becomes a rather enormous task. This paper offers a cutting-edge hybrid deep learning approach of better segmentation and classification of skin lesions. The proposed method incorporates three key stages: preprocessing, segmentation of lesions, and classification of lesions. By the stage of preprocessing, a morphology-based technique takes out hair so as to enhance the segmentation precision to use the cleansed images for subsequent analysis. Segmentation cuts off the lesion from the surrounding skin, giving the classification phase a dedicated area of interest and the ability to clear the background noise that may affect classification rates. The isolation enables the model to better analyze anatomical lesion features in order to achieve accurate benign and malignant classifications. Using ResUNet++, the cutting-edge deep learning architecture, we achieved accurate lesion segmentation. Next, we will modify and use an AlexNet-Random Forest (AlexNet-RF) based classifier for robust lesion classification. The proposed hybrid deep learning model is intensively validated on the Ham10000 data set which is one of the most popular datasets for skin lesions analysis. The obtained results show that the utilized approach, compared to the previous ones, is more effective, giving better segmentation and classification results. This method takes advantage of ResUNet++ strong classification skill and modified AlexNet-Random Forest robustness for more accurate segmentation. There is a high probability that ResUNet++, which is highly proficient at medical image segmentation, can produce better segmentation of lesions than the simpler models. The composition of AlexNet's extraction of features with Random Forest ability to reduce overfitting possibly may be more precise in the classification when compared to using only one model.