Deep Learning-based Computer-aided Diagnosis System Research Articles

Lung Cancer Detection Using Convolutional Neural Networks

Lung cancer is among the most prevalent and deadly cancers worldwide. Accurate diagnosis and early detection are critical for improving lung cancer patient outcomes and survival rates. Thanks to developments in medical imaging technology, computer-aided diagnosis (CAD) systems have shown a great deal of promise in helping radiologists identify and diagnose lung cancer from medical images. Here, we present the use of convolutional neural networks (CNNs) to create an early detection system (CAD) for lung cancer. The suggested approach uses lung computed tomography (CT) scans as input and use a CNN architecture to extract high-level features from the pictures. We use transfer learning to enhance a CNN model trained on a large dataset of CT images. The CNN model has been taught to determine if a specific CT image contains lung cancer or not. We evaluate the performance of the proposed CAD system on a dataset of CT scans of the lungs from different institutions. The trial's results show that our CNN-based CAD system can reliably and precisely identify lung cancer from CT scans. We also show the comparative performance of our proposed system against the state-of-the-art machine learning methods for lung cancer prediction. In conclusion, the suggested CNN-based deep learning-based CAD system has produced encouraging results for lung cancer detection from CT scans. The approach might help radiologists identify and classify lung cancer early on, leading to better patient outcomes and survival rates. The viability and usefulness of the suggested approach in clinical practice require more study

A Practical Computer Aided Diagnosis System for Breast Ultrasound Classifying Lesions into the ACR BI-RADS Assessment

PurposeIn this paper, we propose an open-source deep learning-based computer-aided diagnosis system for breast ultrasound images based on the Breast Imaging Reporting and Data System (BI-RADS).MethodsOur dataset with 8,026 region-of-interest images preprocessed with ten times data augmentation. We compared the classification performance of VGG-16, ResNet-50, and DenseNet-121 and two ensemble methods integrated the single models.ResultsThe ensemble model achieved the best performance, with 81.8% accuracy. Our results show that our model is performant enough to classify Category 2 and Category 4/5 lesions, and data augmentation can improve the classification performance of Category 3.ConclusionOur main contribution is to classify breast ultrasound lesions into BI-RADS assessment classes that place more emphasis on adhering to the BI-RADS medical suggestions including recommending routine follow-up tracing (Category 2), short-term follow-up tracing (Category 3) and biopsies (Category 4/5).

Deep Learning-Based Computer-Aided Diagnosis of Osteochondritis Dissecans of the Humeral Capitellum Using Ultrasound Images.

Ultrasonography is used to diagnose osteochondritis dissecans (OCD) of the humerus; however, its reliability depends on the technical proficiency of the examiner. Recently, computer-aided diagnosis (CAD) using deep learning has been applied in the field of medical science, and high diagnostic accuracy has been reported. We aimed to develop a deep learning-based CAD system for OCD detection on ultrasound images and to evaluate the accuracy of OCD detection using the CAD system. The CAD process comprises 2 steps: humeral capitellum detection using an object-detection algorithm and OCD classification using an image classification network. Four-directional ultrasound images of the elbow of the throwing arm of 196 baseball players (mean age, 11.2 years), including 104 players with normal findings and 92 with OCD, were used for training and validation. An external dataset of 20 baseball players (10 with normal findings and 10 with OCD) was used to evaluate the accuracy of the CAD system. A confusion matrix and the area under the receiver operating characteristic curve (AUC) were used to evaluate the system. Clinical evaluation using the external dataset resulted in high AUCs in all 4 directions: 0.969 for the anterior long axis, 0.966 for the anterior short axis, 0.996 for the posterior long axis, and 0.993 for the posterior short axis. The accuracy of OCD detection thus exceeded 0.9 in all 4 directions. We propose a deep learning-based CAD system to detect OCD lesions on ultrasound images. The CAD system achieved high accuracy in all 4 directions of the elbow. This CAD system with a deep learning model may be useful for OCD screening during medical checkups to reduce the probability of missing an OCD lesion. Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Deep learning-based computer-aided diagnosis system for the automatic detection and classification of lateral cervical lymph nodes on original ultrasound images of papillary thyroid carcinoma: a prospective diagnostic study.

This study aims to develop a deep learning-based computer-aided diagnosis (CAD) system for the automatic detection and classification of lateral cervical lymph nodes (LNs) on original ultrasound images of papillary thyroid carcinoma (PTC) patients. A retrospective data set of 1801 cervical LN ultrasound images from 1675 patients with PTC and a prospective test set including 185 images from 160 patients were collected. Four different deep leaning models were trained and validated in the retrospective data set. The best model was selected for CAD system development and compared with three sonographers in the retrospective and prospective test sets. The Deformable Detection Transformer (DETR) model showed the highest diagnostic efficacy, with a mean average precision score of 86.3% in the retrospective test set, and was therefore used in constructing the CAD system. The detection performance of the CAD system was superior to the junior sonographer and intermediate sonographer with accuracies of 86.3% and 92.4% in the retrospective and prospective test sets, respectively. The classification performance of the CAD system was better than all sonographers with the areas under the curve (AUCs) of 94.4% and 95.2% in the retrospective and prospective test sets, respectively. This study developed a Deformable DETR model-based CAD system for automatically detecting and classifying lateral cervical LNs on original ultrasound images, which showed excellent diagnostic efficacy and clinical utility. It can be an important tool for assisting sonographers in the diagnosis process.

Uncertainty-aware deep learning-based CAD system for breast cancer classification using ultrasound and mammography images

ABSTRACT Breast cancer is one of the most common types of cancer in women. Early and accurate diagnosis of breast cancer can increase the treatment chances and decrease the mortality rate. Thus, the development of accurate and reliable Computer-Aided Diagnosis (CAD) systems using breast cancer images is an immediate priority for early diagnosis. Deep learning methods have had a widespread role in CAD systems. In spite of the advantages, most deep learning-based CADs cannot quantify the uncertainty of their predictions. Uncertainty in the predicted results from a CAD system might endanger people’s life in these crucial steps for breast cancer classification. Therefore, in this study, the Monte Carlo Dropout method is utilised as an uncertainty quantification method. Furthermore, differently from most existing studies, we present an uncertainty-aware deep learning model which can classify accurately from different types of images, namely, mammograms and ultrasound. The results show the proposed model achieved the accuracy of 99.95% in mammogram dataset, namely, DDSM and 98.40% and 91.34% in ultrasound datasets, namely, BUSM and BUSI, respectively. Finally, since we found the proposed model shows high uncertainty in misclassifications, we suggested to use the uncertainty values to find appropriate subsets for further inspection to increase the performance.

Investigating Effective Data Augmentation Techniques for Accurate Gastric Classification in the Development of a Deep Learning-Based Computer-Aided Diagnosis System

Gastric cancer is a significant health concern, particularly in Korea, and its accurate detection is crucial for effective treatment. However, a gastroscopic biopsy can be time-consuming and may, thus, delay diagnosis and treatment. Thus, this study proposed a gastric cancer diagnostic method, CADx, to facilitate a more efficient image analysis. Owing to the challenges in collecting medical image data, small datasets are often used in this field. To overcome this limitation, we used AutoAugment’s ImageNet policy and applied cut-and-paste techniques using a sliding window algorithm to further increase the size of the dataset. The results showed an accuracy of 0.8317 for T-stage 1 and T-stage 4 image classification and an accuracy of 0.8417 for early gastric cancer and normal image classification, indicating improvements of 7 and 9%, respectively. Furthermore, through the application of test-time augmentation to the early gastric cancer and normal image datasets, the image classification accuracy was improved by 5.8% to 0.9000. Overall, the results of this study demonstrate the effectiveness of the proposed augmentation methods for enhancing gastric cancer classification performance.

TS-DSANN: Texture and shape focused dual-stream attention neural network for benign-malignant diagnosis of thyroid nodules in ultrasound images.

Recently, accurate diagnosis of thyroid nodules has played a critical role in precision medicine and healthcare system management. Due to complicated changes in ultrasound features of texture, and similar visual appearance of benign-malignant nodules, the identification of cancerous thyroid lesions from a given ultrasound image still faces challenges for even experienced radiologists. Learning-based computer-aided diagnosis (CAD) systems have accordingly attracted more and more attention recently. However, little research is committed to developing a deep learning-based CAD system that has greater conformity with radiologists' diagnostic decision-making. In this study, we devise a texture and shape focused dual-stream attention neural network, dubbed TS-DSANN. Specifically, in the texture focused stream, we utilize the ImageNet pre-trained ResNet34 to guide the network to recognize texture-related nodule attributes. Meanwhile, in the shape focused stream, in addition to using ResNet34 backbone, jointly learning from scratch with the contour obtained by contour detection module to enhance the extraction of shape features. Afterward, we employ a concatenation operation to aggregate the abovementioned two stream networks for capturing richer and more representative features. Finally, we further utilize an online class activation mapping mechanism to assist the dual-stream network in generating a localization heatmap to obtain more visualization attention to the nodule from the whole image, and supervise classifier's attention in decision-making. Experimental results conducted on the two-center thyroid nodule ultrasound datasets verify that our proposed method has improved the classification performance, superior to the state-of-the-art methods.

Identification of TLE Focus from EEG Signals by Using Deep Learning Approach.

Temporal lobe epilepsy, a neurological disease that causes seizures as a result of excessive neural activities in the brain, is the most common type of focal seizure, accounting for 30-35% of all epilepsies. Detection of epilepsy and localization of epileptic focus are essential for treatment planning and epilepsy surgery. Currently, epileptic focus is decided by expert physician by examining the EEG records and determining EEG channel where epileptic patterns begins and continues intensely during seizure. Examination of long EEG recordings is very time-consuming process, requires attention and decision can vary depending on physician. In this study, to assist physicians in detecting epileptic focus side from EEG recordings, a novel deep learning-based computer-aided diagnosis system is presented. In the proposed framework, ictal epochs are detected using long short-term memory network fed with EEG subband features obtained by discrete wavelet transform, and then, epileptic focus identification is realized by using asymmetry score. This algorithm was tested on EEG database obtained from the Ankara University hospital. Experimental results showed ictal and interictal epochs were classified with accuracy of 86.84%, sensitivity of 86.96% and specificity of 89.68% on Ankara University hospital dataset, and 96.67% success rate was obtained on Bonn EEG dataset. In addition, epileptic focus was identified with accuracy of 96.10%, sensitivity of 100% and specificity of 93.80% by using the proposed deep learning-based algorithm and university hospital dataset. These results showed that proposed method can be used properly in clinical applications, epilepsy treatment and surgical planning as a medical decision support system.

The Role of a Deep Learning-Based Computer-Aided Diagnosis System and Elastography in Reducing Unnecessary Breast Lesion Biopsies

ObjectivesUltrasound examination has inter-observer and intra-observer variability and a high false-positive rate. The aim of this study was to evaluate the value of the combined use of a deep learning-based computer-aided diagnosis (CAD) system and ultrasound elastography with conventional ultrasound (US) in increasing specificity and reducing unnecessary breast lesions biopsies. Materials and MethodsConventional US, CAD system, and strain elastography (SE) were retrospectively performed on 216 breast lesions before biopsy or surgery. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and biopsy rate were compared between conventional US and the combination of conventional US, SE, and CAD system. ResultsOf 216 lesions, 54 were malignant and 162 were benign. The addition of CAD system and SE to conventional US increased the AUC from 0.716 to 0.910 and specificity from 46.9% to 85.8% without a loss in sensitivity while 89.2% (66 of 74) of benign lesions in Breast Imaging Reporting and Data System (BI-RADS) category 4A lesions would avoid unnecessary biopsies. ConclusionThe addition of CAD system and SE to conventional US improved specificity and AUC without loss of sensitivity, and reduced unnecessary biopsies.

Classification of large-scale image database of various skin diseases using deep learning.

The purpose of this study was to develop a deep learning-based computer-aided diagnosis system for skin disease classification using photographic images of patients. The targets are 59 skin diseases, including localized and diffuse diseases captured by photographic cameras, resulting in highly diverse images in terms of the appearance of the diseases or photographic conditions. ResNet-18 is used as a baseline model for classification and is reinforced by metric learning to boost generalization in classification by avoiding the overfitting of the training data and increasing the reliability of CADx for dermatologists. Patient-wise classification is performed by aggregating the inference vectors of all the input patient images. The experiment using 70,196 images of 13,038 patients demonstrated that classification accuracy was significantly improved by both metric learning and aggregation, resulting in patient accuracies of 0.579 for Top-1, 0.793 for Top-3, and 0.863 for Top-5. The McNemar test showed that the improvements achieved by the proposed method were statistically significant. This study presents a deep learning-based classification of 59 skin diseases using multiple photographic images of a patient. The experimental results demonstrated that the proposed classification reinforced by metric learning and aggregation of multiple input images was effective in the classification of patients with diverse skin diseases and imaging conditions.

Deep neural network approaches for detecting gastric polyps in endoscopic images.

Gastrointestinal endoscopy is the primary method used for the diagnosis and treatment of gastric polyps. The early detection and removal of polyps is vitally important in preventing cancer development. Many studies indicate that a high workload can contribute to misdiagnosing gastric polyps, even for experienced physicians. In this study, we aimed to establish a deep learning-based computer-aided diagnosis system for automatic gastric polyp detection. A private gastric polyp dataset was generated for this purpose consisting of 2195 endoscopic images and 3031 polyp labels. Retrospective gastrointestinal endoscopy data from the Karadeniz Technical University, Farabi Hospital, were used in the study. YOLOv4, CenterNet, EfficientNet, Cross Stage ResNext50-SPP, YOLOv3, YOLOv3-SPP, Single Shot Detection, and Faster Regional CNN deep learning models were implemented and assessed to determine the most efficient model for precancerous gastric polyp detection. The dataset was split 70% and 30% for training and testing all the implemented models. YOLOv4 was determined to be the most accurate model, with an 87.95% mean average precision. We also evaluated all the deep learning models using a public gastric polyp dataset as the test data. The results show that YOLOv4 has significant potential applicability in detecting gastric polyps and can be used effectively in gastrointestinal CAD systems. Gastric Polyp Detection Process using Deep Learning with Private Dataset.

A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study.

The aim of this study was to evaluate the effect of a deep learning based computer-aided diagnosis (DL-CAD) system on radiologists' interpretation accuracy and efficiency in reading biparametric prostate magnetic resonance imaging scans. We selected 100 consecutive prostate magnetic resonance imaging cases from a publicly available data set (PROSTATEx Challenge) with and without histopathologically confirmed prostate cancer. Seven board-certified radiologists were tasked to read each case twice in 2 reading blocks (with and without the assistance of a DL-CAD), with a separation between the 2 reading sessions of at least 2 weeks. Reading tasks were to localize and classify lesions according to Prostate Imaging Reporting and Data System (PI-RADS) v2.0 and to assign a radiologist's level of suspicion score (scale from 1-5 in 0.5 increments; 1, benign; 5, malignant). Ground truth was established by consensus readings of 3 experienced radiologists. The detection performance (receiver operating characteristic curves), variability (Fleiss κ), and average reading time without DL-CAD assistance were evaluated. The average accuracy of radiologists in terms of area under the curve in detecting clinically significant cases (PI-RADS ≥4) was 0.84 (95% confidence interval [CI], 0.79-0.89), whereas the same using DL-CAD was 0.88 (95% CI, 0.83-0.94) with an improvement of 4.4% (95% CI, 1.1%-7.7%; P = 0.010). Interreader concordance (in terms of Fleiss κ) increased from 0.22 to 0.36 (P = 0.003). Accuracy of radiologists in detecting cases with PI-RADS ≥3 was improved by 2.9% (P = 0.10). The median reading time in the unaided/aided scenario was reduced by 21% from 103 to 81 seconds (P < 0.001). Using a DL-CAD system increased the diagnostic accuracy in detecting highly suspicious prostate lesions and reduced both the interreader variability and the reading time.

Diagnostic Value of Breast Lesions Between Deep Learning-Based Computer-Aided Diagnosis System and Experienced Radiologists: Comparison the Performance Between Symptomatic and Asymptomatic Patients.

Purpose: The purpose of this study was to compare the diagnostic performance of breast lesions between deep learning-based computer-aided diagnosis (deep learning-based CAD) system and experienced radiologists and to compare the performance between symptomatic and asymptomatic patients.Methods: From January to December 2018, a total of 451 breast lesions in 389 consecutive patients were examined (mean age 46.86 ± 13.03 years, range 19–84 years) by both ultrasound and deep learning-based CAD system, all of which were biopsied, and the pathological results were obtained. The lesions were diagnosed by two experienced radiologists according to the fifth edition Breast Imaging Reporting and Data System (BI-RADS). The final deep learning-based CAD assessments were dichotomized as possibly benign or possibly malignant. The diagnostic performances of the radiologists and deep learning-based CAD were calculated and compared for asymptomatic patients and symptomatic patients.Results: There were 206 asymptomatic screening patients with 235 lesions (mean age 45.06 ± 10.90 years, range 21–73 years) and 183 symptomatic patients with 216 lesions (mean age 50.03 ± 14.97 years, range 19–84 years). The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy and area under the receiver operating characteristic curve (AUC) of the deep learning-based CAD in asymptomatic patients were 93.8, 83.9, 75.0, 96.3, 87.2, and 0.89%, respectively. In asymptomatic patients, the specificity (83.9 vs. 66.5%, p < 0.001), PPV (75.0 vs. 59.4%, p = 0.013), accuracy (87.2 vs. 76.2%, p = 0.002) and AUC (0.89 to 0.81, p = 0.0013) of CAD were all significantly higher than those of the experienced radiologists. The sensitivity (93.8 vs. 80.0%), specificity (83.9 vs. 61.8%,), accuracy (87.2 vs. 73.6%) and AUC (0.89 vs. 0.71) of CAD were all higher for asymptomatic patients than for symptomatic patients. If the BI-RADS 4a lesions diagnosed by the radiologists in asymptomatic patients were downgraded to BI-RADS 3 according to the CAD, then 54.8% (23/42) of the lesions would avoid biopsy without missing the malignancy.Conclusion: The deep learning-based CAD system had better performance in asymptomatic patients than in symptomatic patients and could be a promising complementary tool to ultrasound for increasing diagnostic specificity and avoiding unnecessary biopsies in asymptomatic screening patients.

Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future

Background: Gastrointestinal (GI) tract abnormalities are most common across the world, and it is a significant threat to the health of human beings. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly procedure for the diagnosis of GI tract abnormalities. However, it is very time consuming and tiresome task for physicians due to length of endoscopy videos. Thus computer-aided diagnosis (CAD) system is a must. Methods: This systematic review aims to investigate state-of-the-art CAD systems for automatic abnormality detection in capsule endoscopy by examining publications from scientific databases namely IEEE Xplore, Science Direct, Springer, and Scopus. Results: Based on defined search criteria and applied inclusion and exclusion criteria, 44 articles are included out of 187. This study presents the current status and analysis of CAD systems for capsule endoscopy. Conclusion: Publicly available larger dataset and a deep learning based CAD system may help to improve the efficiency of automated abnormality detection in capsule endoscopy.

Computer-Aided Diagnosis for Capsule Endoscopy: From Inception to Future

Background: Gastrointestinal (GI) tract abnormalities are most common across the world, and it is a significant threat to the health of human beings. Capsule endoscopy is a non-sedative, non-invasive and patient-friendly procedure for the diagnosis of GI tract abnormalities. However, it is very time consuming and tiresome task for physicians due to length of endoscopy videos. Thus computer-aided diagnosis (CAD) system is a must. Methods: This systematic review aims to investigate state-of-the-art CAD systems for automatic abnormality detection in capsule endoscopy by examining publications from scientific databases namely IEEE Xplore, Science Direct, Springer, and Scopus. Results: Based on defined search criteria and applied inclusion and exclusion criteria, 44 articles are included out of 187. This study presents the current status and analysis of CAD systems for capsule endoscopy. Conclusion: Publicly available larger dataset and a deep learning based CAD system may help to improve the efficiency of automated abnormality detection in capsule endoscopy.

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT.

To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in patients with thyroid cancer. A total of 995 axial CT images that included benign (n = 647) and malignant (n = 348) lymph nodes were collected from 202 patients with thyroid cancer who underwent CT for surgical planning between July 2017 and January 2018. The datasets were randomly split into training (79.0%), validation (10.5%), and test (10.5%) datasets. Eight deep convolutional neural network (CNN) models were used to classify the images into metastatic or benign lymph nodes. Pretrained networks were used on the ImageNet and the best-performing algorithm was selected. Class-specific discriminative regions were visualized with attention heatmap using a global average pooling method. The area under the ROC curve (AUROC) for the tested algorithms ranged from 0.909 to 0.953. The sensitivity, specificity, and accuracy of the best-performing algorithm were all 90.4%, respectively. Attention heatmap highlighted important subregions for further clinical review. A deep learning-based CAD system could accurately classify cervical LNM in patients with thyroid cancer on preoperative CT with an AUROC of 0.953. Whether this approach has clinical utility will require evaluation in a clinical setting. • A deep learning-based CAD system could accurately classify cervical lymph node metastasis. The AUROC for the eight tested algorithms ranged from 0.909 to 0.953. • Of the eight models, the ResNet50 algorithm was the best-performing model for the validation dataset with 0.953 AUROC. The sensitivity, specificity, and accuracy of the ResNet50 model were all 90.4%, respectively, in the test dataset. • Based on its high accuracy of 90.4%, we consider that this model may be useful in a clinical setting to detect LNM on preoperative CT in patients with thyroid cancer.

Deep Learning-Based Computer-Aided Diagnosis System for Localization and Diagnosis of Metastatic Lymph Nodes on Ultrasound: A Pilot Study.

The presence of metastatic lymph nodes is a prognostic indicator for patients with thyroid carcinomas and is an important determinant of clinical decision making. However, evaluating neck lymph nodes requires experience and is labor- and time-intensive. Therefore, the development of a computer-aided diagnosis (CAD) system to identify and differentiate metastatic lymph nodes may be useful. From January 2008 to December 2016, we retrieved clinical records for 804 consecutive patients with 812 lymph nodes. The status of all lymph nodes was confirmed by fine-needle aspiration. The datasets were split into training (263 benign and 286 metastatic lymph nodes), validation (30 benign and 33 metastatic lymph nodes), and test (100 benign and 100 metastatic lymph nodes). Using the VGG-Class Activation Map model, we developed a CAD system to localize and differentiate the metastatic lymph nodes. We then evaluated the diagnostic performance of this CAD system in our test set. In the test set, the accuracy, sensitivity, and specificity of our model for predicting lymph node malignancy were 83.0%, 79.5%, and 87.5%, respectively. The CAD system clearly detected the locations of the lymph nodes, which not only provided identifying data, but also demonstrated the basis of decisions. We developed a deep learning-based CAD system for the localization and differentiation of metastatic lymph nodes from thyroid cancer on ultrasound. This CAD system is highly sensitive and may be used as a screening tool; however, as it is relatively less specific, the screening results should be validated by experienced physicians.

A Deep Learning-based Computer-aided Diagnosis System for Mammographic Lesion Detection

A Deep Learning-based Computer-aided Diagnosis System for Mammographic Lesion Detection