Computer-aided Diagnosis Scheme Research Articles

Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis.

Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular structure. Hence, for an efficient and timely diagnosis it is critical to develop some methods being able to accurately recognize each Bifurcation of Interest (BoI). Indeed, an automatic extraction of the bifurcations presenting the higher risk of developing an ICA would offer the neuroradiologists a quick glance at the most alarming areas. Due to the recent efforts on Artificial Intelligence, Deep Learning turned out to be the best performing technology for many pattern recognition tasks. Moreover, various methods have been particularly designed for medical image analysis purposes. This study intends to assist the neuroradiologists to promptly locate any bifurcation presenting a high risk of ICA occurrence. It can be seen as a Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates the access to the regions of interest within the MRI. In this work, we propose a method for a fully automatic detection and recognition of the bifurcations of interest forming the Circle of Willis. Several neural networks architectures have been tested, and we thoroughly evaluate the bifurcation recognition rate.

Real time mass classification for mammographic images: a Driven CADx scheme

Computer-Aided Diagnosis (CADx) schemes have been proposed to serve as a supplementary image analysis tool in mammography. Experienced radiologists tend to be more assertive to such schemes in assisting their interpretation rather than solely relying on their ability to detect suspicious signals. This study focuses on a simplified version of a previously developed mammography CADx scheme, which was initially designed for digitized film, but is now specifically aimed at classifying breast nodules marked as regions of interest on digital images. This “driven” CADx scheme provides prompt indications regarding whether the selected nodule is deemed normal or suspicious. Its performance was evaluated through tests conducted on different mammograms sets – one with large number of images selected from DDSM database for training, testing and validation of classification parameters, and other comprising direct digital images from InBreast database. Remarkably, similar rates were observed for sensitivity, specificity and accuracy across these two sets (83%, 67% and 72%, respectively). The classification attributes were associated to contour, density and texture. Furthermore, a third test was conducted involving radiologists analyzing digital mammograms obtained from a specific full field digital mammography (FFDM) unit. Results showed that the Driven CADx scheme positively influenced the final diagnoses made by 3 radiologists, consistently increasing accuracy rates. This promising result allows establishing this software as a valuable tool for radiologists in the analysis of masses in digital mammography. The scheme can be implemented on any operating system, or even accessed online.

A multi-stage fusion framework to classify breast lesions using deep learning and radiomics features computed from four-view mammograms.

Developing computer aided diagnosis (CAD) schemes of mammograms to classify between malignant and benign breast lesions has attracted a lot of research attention over the last several decades. However, unlike radiologists who make diagnostic decisions based on the fusion of image features extracted from multi-view mammograms, most CAD schemes are single-view-based schemes, which limit CAD performance and clinical utility. This study aims to develop and test a novel CAD framework that optimally fuses information extracted from ipsilateral views of bilateral mammograms using both deep transfer learning (DTL) and radiomics feature extraction methods. An image dataset containing 353 benign and 611 malignant cases is assembled. Each case contains four images: the craniocaudal (CC) and mediolateral oblique (MLO) view of the left and right breast. First, we extract four matching regions of interest (ROIs) from images that surround centers of two suspicious lesion regions seen in CC and MLO views, as well as matching ROIs in the contralateral breasts. Next, the handcrafted radiomics (HCRs) features and VGG16 model-generated automated features are extracted from each ROI resulting in eight feature vectors. Then, after reducing feature dimensionality and quantifying the bilateral and ipsilateral asymmetry of four ROIs to yield four new feature vectors, we test four fusion methods to build three support vector machine (SVM) classifiers by an optimal fusion of asymmetrical image features extracted from four view images. Using a 10-fold cross-validation method, results show that a SVM classifier trained using an optimal fusion of four view images yields the highest classification performance (AUC=0.876±0.031), which significantly outperforms SVM classifiers trained using one projection view alone, AUC=0.817±0.026 and 0.792±0.026 for the CC and MLO view of bilateral mammograms, respectively (p<0.001). The study demonstrates that the shift from single-view CAD to four-view CAD and the inclusion of both DTL and radiomics features significantly increases CAD performance in distinguishing between malignant and benign breast lesions.

Lung nodule detection in chest CT images based on vision transformer network with Bayesian optimization

PurposeLung cancer patients are usually diagnosed by the Computed tomography (CT) modality with the help of radiologists. Frequently, radiologists manually analyse CT images, which is a big task that consumes more time and results in inaccuracy. Therefore, this study proposes an automated computer-aided diagnosis (CAD) scheme based on Vision Transformer architecture with Bayesian Optimisation for lung nodule detection to assist radiologists in decision-making. MethodsThe Vision Transformer architecture relies on the shifted window-based vision transformer encoders to improve the processing and propagation efficiency of the lung nodule features. The baseline detection model is developed by integrating shifted window-based vision transformer for deep nodule feature extractions and a region proposal network to predict candidate nodules with low network complexity. The Bayesian Optimisation with the Gaussian method was used to optimise the hyperparameter values from the baseline model suitable for lung nodule detection on chest CT images. ResultsWe used 888 CT images with 10-fold cross-validation from the Lung Nodule Analysis 2016 (LUNA16) public dataset to validate the proposed network. Free-response receiver operating characteristics and competition performance metrics (CPM) was used for the performance assessment. The proposed architecture obtained the highest detection sensitivity of 98.39% and the CPM score of 0.909 with few network parameters. ConclusionThis study proposes a CAD system based on a vision transformer network determined by Bayesian Optimisation for lung nodule detection in chest CT images. The empirical results prove the proposed algorithm's effectiveness compared to the state-of-the-art methods.

Skin cancer image recognition based on similarity clustering and attention transfer.

Melanoma is a tumor caused by melanocytes with a high degree of malignancy, easy local recurrence, distant metastasis, and poor prognosis. It is also difficult to be detected by inexperienced dermatologist due to their similar appearances, such as color, shape, and contour. To develop and test a new computer-aided diagnosis scheme to detect melanoma skin cancer. In this new scheme, the unsupervised clustering based on deep metric learning is first conducted to make images with high similarity together and the corresponding model weights are utilized as teacher-model for the next stage. Second, benefit from the knowledge distillation, the attention transfer is adopted to make the classification model enable to learn the similarity features and information of categories simultaneously which improve the diagnosis accuracy than the common classification method. In validation sets, 8 categories were included, and 2443 samples were calculated. The highest accuracy of the new scheme is 0.7253, which is 5% points higher than the baseline (0.6794). Specifically, the F1-Score of three malignant lesions BCC (Basal cell carcinoma), SCC (Squamous cell carcinomas), and MEL (Melanoma) increase from 0.65 to 0.73, 0.28 to 0.37, and 0.54 to 0.58, respectively. In two test sets of HAN including 3844 samples and BCN including 6375 samples, the highest accuracies are 0.68 and 0.53 for HAM and BCN datasets, respectively, which are higher than the baseline (0.649 and 0.516). Additionally, F1 scores of BCC, SCC, MEL are 0.49, 0.2, 0.45 in HAM dataset and 0.6, 0.14, 0.55 in BCN dataset, respectively, which are also higher than F1 scores the results of baseline. This study demonstrates that the similarity clustering method enables to extract the related feature information to gather similar images together. Moreover, based on the attention transfer, the proposed classification framework can improve total accuracy and F1-score of skin lesion diagnosis.

Predicting muscle invasion in bladder cancer based on MRI: A comparison of radiomics, and single-task and multi-task deep learning

Background and objectivesRadiomics and deep learning are two popular technologies used to develop computer-aided detection and diagnosis schemes for analysing medical images. This study aimed to compare the effectiveness of radiomics, single-task deep learning (DL) and multi-task DL methods in predicting muscle-invasive bladder cancer (MIBC) status based on T2-weighted imaging (T2WI). MethodsA total of 121 tumours (93 for training, from Centre 1; 28 for testing, from Centre 2) were included. MIBC was confirmed with pathological examination. A radiomics model, a single-task model, and a multi-task model based on T2WI were constructed in the training cohort with five-fold cross-validation, and validation was conducted in the external test cohort. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of each model. DeLong's test and a permutation test were used to compare the performance of the models. ResultsThe area under the ROC curve (AUC) values of the radiomics, single-task and multi-task models in the training cohort were: 0.920, 0.933 and 0.932, respectively; and were 0.844, 0.884 and 0.932, respectively, in the test cohort. The multi-task model achieved better performance in the test cohort than did the other models. No statistically significant differences in AUC values and Kappa coefficients were observed between pairwise models, in either the training or test cohorts. According to the Grad-CAM feature visualization results, the multi-task model focused more on the diseased tissue area in some samples of the test cohort compared with the single-task model. ConclusionsThe T2WI-based radiomics, single-task, and multi-task models all exhibited good diagnostic performance in preoperatively predicting MIBC, in which the multi-task model had the best diagnostic performance. Compared with the radiomics method, our multi-task DL method had the advantage of saving time and effort. Compared with the single-task DL method, our multi-task DL method had the advantage of being more lesion-focused and more reliable for clinical reference.

Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis.

Breast Cancer (BC) is a major health issue in women of the age group above 45. Identification of BC at an earlier stage is important to reduce the mortality rate. Image-based noninvasive methods are used for early detection and for providing appropriate treatment. Computer-Aided Diagnosis (CAD) schemes can support radiologists in making correct decisions. Computational intelligence paradigms such as Machine Learning (ML) and Deep Learning (DL) have been used in the recent past in CAD systems to accelerate diagnosis. ML techniques are feature driven and require a high amount of domain expertise. However, DL approaches make decisions directly from the image. The current advancement in DL approaches for early diagnosis of BC is the motivation behind this review. This article throws light on various types of CAD approaches used in BC detection and diagnosis. A survey on DL, Transfer Learning, and DL-based CAD approaches for the diagnosis of BC is presented in detail. A comparative study on techniques, datasets, and performance metrics used in state-of-the-art literature in BC diagnosis is also summarized. The proposed work provides a review of recent advancements in DL techniques for enhancing BC diagnosis.

Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new AI-based quantitative image feature analysis models in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms.

Deep convolutional neural networks (CNNs) have been widely used in various medical imaging tasks. However, due to the intrinsic locality of convolution operations, CNNs generally cannot model long-range dependencies well, which are important for accurately identifying or mapping corresponding breast lesion features computed from unregistered multiple mammograms. This motivated us to leverage the architecture of Multi-view Vision Transformers to capture long-range relationships of multiple mammograms from the same patient in one examination. For this purpose, we employed local transformer blocks to separately learn patch relationships within four mammograms acquired from two-view (CC/MLO) of two-side (right/left) breasts. The outputs from different views and sides were concatenated and fed into global transformer blocks, to jointly learn patch relationships between four images representing two different views of the left and right breasts. To evaluate the proposed model, we retrospectively assembled a dataset involving 949 sets of mammograms, which included 470 malignant cases and 479 normal or benign cases. We trained and evaluated the model using a five-fold cross-validation method. Without any arduous preprocessing steps (e.g., optimal window cropping, chest wall or pectoral muscle removal, two-view image registration, etc.), our four-image (two-view-two-side) transformer-based model achieves case classification performance with an area under ROC curve (AUC = 0.818 ± 0.039), which significantly outperforms AUC = 0.784 ± 0.016 achieved by the state-of-the-art multi-view CNNs (p = 0.009). It also outperforms two one-view-two-side models that achieve AUC of 0.724 ± 0.013 (CC view) and 0.769 ± 0.036 (MLO view), respectively. The study demonstrates the potential of using transformers to develop high-performing computer-aided diagnosis schemes that combine four mammograms.

A Comparison of Computer-Aided Diagnosis Schemes Optimized Using Radiomics and Deep Transfer Learning Methods.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diagnosis (CAD) schemes of medical images. This study aims to investigate and to compare the advantages and the potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3000 digital mammograms was assembled in which 1496 images depicted malignant lesions and 1504 images depicted benign lesions. Two CAD schemes were developed to classify breast lesions. The first scheme was developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme was built based on a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes were trained and tested using a 10-fold cross-validation method. Several score fusion methods were also investigated to classify breast lesions. CAD performances were evaluated and compared by the areas under the ROC curve (AUC). Results: The ResNet50 model-based CAD scheme yielded AUC = 0.85 ± 0.02, which was significantly higher than the radiomics feature-based CAD scheme with AUC = 0.77 ± 0.02 (p < 0.01). Additionally, the fusion of classification scores generated by the two CAD schemes did not further improve classification performance. Conclusion: This study demonstrates that using deep transfer learning is more efficient to develop CAD schemes and it enables a higher lesion classification performance than CAD schemes developed using radiomics-based technology.

Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images.

Breast cancer is the most menacing cancer among all types of cancer in women around the globe. Early diagnosis is the only way to increase the treatment options which then decreases the death rate and increases the chance of survival in patients. However, it is a challenging task to differentiate abnormal breast tissues from normal tissues because of their structure and unclear boundaries. Therefore, early and accurate diagnosis and classification of breast lesions into malignant or benign lesions is an active domain of research. Over the decade, numerous artificial neural network (ANN)-based techniques were adopted in order to diagnose and classify breast cancer due to the unique characteristics of learning key features from complex data via a training process. However, these schemes have limitations like slow convergence and longer training time. To address the above mentioned issues, this paper employs a meta-heuristic algorithm for tuning the parameters of the neural network. The main novelty of this work is the computer-aided diagnosis scheme for detecting abnormalities in breast ultrasound images by integrating a wavelet neural network (WNN) and the grey wolf optimization (GWO) algorithm. Here, breast ultrasound (US) images are preprocessed with a sigmoid filter followed by interference-based despeckling and then by anisotropic diffusion. The automatic segmentation algorithm is adopted to extract the region of interest, and subsequently morphological and texture features are computed. Finally, the GWO-tuned WNN is exploited to accomplish the classification task. The classification performance of the proposed scheme is validated on 346 ultrasound images. Efficiency of the proposed methodology is evaluated by computing the confusion matrix and receiver operating characteristic (ROC) curve. Numerical analysis revealed that the proposed work can yield higher classification accuracy when compared to the prevailing methods and thereby proves its potential in effective breast tumor detection and classification. The proposed GWO-WNN method (98%) gives better accuracy than other methods like SOM-SVM (87.5), LOFA-SVM (93.62%), MBA-RF (96.85%), and BAS-BPNN (96.3%)

Tooth detection for each tooth type by application of faster R-CNNs to divided analysis areas of dental panoramic X-ray images.

This study aimed to propose a computerized method for detecting the tooth region for each tooth type as the initial stage in the development of a computer-aided diagnosis (CAD) scheme for dental panoramic X-ray images. Our database consists of 160 panoramic dental X-ray images obtained from 160 adult patients. To reduce false positives (FPs), the proposed method first extracts a rectangular area including all teeth from a dental panoramic X-ray image with a faster region using a convolutional neural network (Faster R-CNN). From the rectangular area including all teeth, six divided areas are then extracted with Faster R-CNN: top left, top center, top right, bottom left, bottom center, and bottom right. Faster R-CNNs for detecting tooth regions for each tooth type were trained individually for each of the divided areas that narrowed down the target tooth types. By applying these Faster R-CNNs to each divided area, the bounding boxes of each tooth were detected and classified into 32 tooth types. A k-fold cross-validation method with k = 4 was used for training and testing the proposed method. The detection rate for each tooth, number of FPs per image, mean intersection over union for each tooth, and classification accuracy for the 32 tooth types were 98.9%, 0.415, 0.748, and 91.7%, respectively, showing an improvement compared to the application of the Faster R-CNN once to the entire image (98.0%, 1.194, 0.736, and 88.8%).

Applying a radiomics-based CAD scheme to classify between malignant and benign pancreatic tumors using CT images.

Pancreatic cancer is one of the most aggressive cancers with approximate 10% five-year survival rate. To reduce mortality rate, accurate detection and diagnose of suspicious pancreatic tumors at an early stage plays an important role. To develop and test a new radiomics-based computer-aided diagnosis (CAD) scheme of computed tomography (CT) images to detect and classify suspicious pancreatic tumors. A retrospective dataset consisting of 77 patients who had suspicious pancreatic tumors detected on CT images was assembled in which 33 tumors are malignant. A CAD scheme was developed using the following 5 steps namely, (1) apply an image pre-processing algorithm to filter and reduce image noise, (2) use a deep learning model to detect and segment pancreas region, (3) apply a modified region growing algorithm to segment tumor region, (4) compute and select optimal radiomics features, and (5) train and test a support vector machine (SVM) model to classify the detected pancreatic tumor using a leave-one-case-out cross-validation method. By using the area under receiver operating characteristic (ROC) curve (AUC) as an evaluation index, SVM model yields AUC = 0.750 with 95% confidence interval [0.624, 0.885] to classify pancreatic tumors. Study results indicate that radiomics features computed from CT images contain useful information associated with risk of tumor malignancy. This study also built a foundation to support further effort to develop and optimize CAD schemes with more advanced image processing and machine learning methods to more accurately and robustly detect and classify pancreatic tumors in future.

Deep learning-based body part recognition algorithm for three-dimensional medical images.

The automatic recognition of human body parts in three-dimensional medical images is important in many clinical applications. However, methods presented in prior studies have mainly classified each two-dimensional (2D) slice independently rather than recognizing a batch of consecutive slices as a specific body part. In this study, we aim to develop a deep learning-based method designed to automatically divide computed tomography (CT) and magnetic resonance imaging (MRI) scans into five consecutive body parts: head, neck, chest, abdomen, and pelvis. A deep learning framework was developed to recognize body parts in two stages. In the first preclassification stage, a convolutional neural network (CNN) using the GoogLeNet Inception v3 architecture and a long short-term memory (LSTM) network were combined to classify each 2D slice; the CNN extracted information from a single slice, whereas the LSTM employed rich contextual information among consecutive slices. In the second postprocessing stage, the input scan was further partitioned into consecutive body parts by identifying the optimal boundaries between them based on the slice classification results of the first stage. To evaluate the performance of the proposed method, 662 CT and 1434 MRI scans were used. Our method achieved a very good performance in 2D slice classification compared with state-of-the-art methods, with overall classification accuracies of 97.3% and 98.2% for CT and MRI scans, respectively. Moreover, our method further divided whole scans into consecutive body parts with mean boundary errors of 8.9 and 3.5mm for CT and MRI data, respectively. The proposed method significantly improved the slice classification accuracy compared with state-of-the-art methods, and further accurately divided CT and MRI scans into consecutive body parts based on the results of slice classification. The developed method can be employed as an important step in various computer-aided diagnosis and medical image analysis schemes.

Improving mammography lesion classification by optimal fusion of handcrafted and deep transfer learning features

Objective. Handcrafted radiomics features or deep learning model-generated automated features are commonly used to develop computer-aided diagnosis schemes of medical images. The objective of this study is to test the hypothesis that handcrafted and automated features contain complementary classification information and fusion of these two types of features can improve CAD performance. Approach. We retrospectively assembled a dataset involving 1535 lesions (740 malignant and 795 benign). Regions of interest (ROI) surrounding suspicious lesions are extracted and two types of features are computed from each ROI. The first one includes 40 radiomic features and the second one includes automated features computed from a VGG16 network using a transfer learning method. A single channel ROI image is converted to three channel pseudo-ROI images by stacking the original image, a bilateral filtered image, and a histogram equalized image. Two VGG16 models using pseudo-ROIs and 3 stacked original ROIs without pre-processing are used to extract automated features. Five linear support vector machines (SVM) are built using the optimally selected feature vectors from the handcrafted features, two sets of VGG16 model-generated automated features, and the fusion of handcrafted and each set of automated features, respectively. Main Results. Using a 10-fold cross-validation, the fusion SVM using pseudo-ROIs yields the highest lesion classification performance with area under ROC curve (AUC = 0.756 ± 0.042), which is significantly higher than those yielded by other SVMs trained using handcrafted or automated features only (p < 0.05). Significance. This study demonstrates that both handcrafted and automated futures contain useful information to classify breast lesions. Fusion of these two types of features can further increase CAD performance.

Malaria detection through digital microscopic imaging using Deep Greedy Network with transfer learning.

Purpose: In conventional diagnosis, the visual inspection of the malaria parasite Plasmodium falciparum in infected red blood cells under a microscope, is done manually by pathologists, which is both laborious and error-prone. Recent studies on automating this process have been conducted using artificial intelligence and feature selection of positional and morphological features from blood smear cell images using convolutional neural network (CNN). However, most deep CNN models do not perform well as per the expectation on small datasets. Approach: In this context, we propose a comprehensive computer-aided diagnosis scheme for automating the detection of malaria parasites in thin blood smear images using deep CNN, where transfer learning is used for optimizing the feature selection process. As an extra layer of security, layer embeddings are extracted from the intermediate convolutional layers using the feature matrix to cross-check the selection of features in the intermediate layers. The proposal includes the utilization of the ResNet 152 model integrated with the Deep Greedy Network for training, which produces an enhanced quality of prediction. Results: The performance of the proposed hybrid model has been evaluated concerning the evaluation metrics such as accuracy, precision, recall, specificity, and F1-score, which has been further compared with the pre-existing deep learning algorithms. Conclusions: The comparative analysis of the results reported based on the accuracy metrics demonstrates promising outcomes concerning the other models. Lastly, the embedding extraction from the intermediate hidden layers and their visual analysis also provides an opportunity for manual verification of the performance of the trained model.

Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features

Objectives. This study aims to develop a computer-aided diagnosis (CADx) scheme to classify between benign and malignant ground glass nodules (GGNs), and fuse deep leaning and radiomics imaging features to improve the classification performance. Methods. We first retrospectively collected 513 surgery histopathology confirmed GGNs from two centers. Among these GGNs, 100 were benign and 413 were malignant. All malignant tumors were stage I lung adenocarcinoma. To segment GGNs, we applied a deep convolutional neural network and residual architecture to train and build a 3D U-Net. Then, based on the pre-trained U-Net, we used a transfer learning approach to build a deep neural network (DNN) to classify between benign and malignant GGNs. With the GGN segmentation results generated by 3D U-Net, we also developed a CT radiomics model by adopting a series of image processing techniques, i.e. radiomics feature extraction, feature selection, synthetic minority over-sampling technique, and support vector machine classifier training/testing, etc. Finally, we applied an information fusion method to fuse the prediction scores generated by DNN based CADx model and CT-radiomics based model. To evaluate the proposed model performance, we conducted a comparison experiment by testing on an independent testing dataset. Results. Comparing with DNN model and radiomics model, our fusion model yielded a significant higher area under a receiver operating characteristic curve (AUC) value of 0.73 ± 0.06 (P < 0.01). The fusion model generated an accuracy of 75.6%, F1 score of 84.6%, weighted average F1 score of 70.3%, and Matthews correlation coefficient of 43.6%, which were higher than the DNN model and radiomics model individually. Conclusions. Our experimental results demonstrated that (1) applying a CADx scheme was feasible to diagnosis of early-stage lung adenocarcinoma, (2) deep image features and radiomics features provided complementary information in classifying benign and malignant GGNs, and (3) it was an effective way to build DNN model with limited dataset by using transfer learning. Thus, to build a robust image analysis based CADx model, one can combine different types of image features to decode the imaging phenotypes of GGN.

Applying a Random Projection Algorithm to Optimize Machine Learning Model for Breast Lesion Classification.

Since computer-aided diagnosis (CAD) schemes of medical images usually computes large number of image features, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models, the objective of this study is to investigate feasibility of applying a random projection algorithm (RPA) to build an optimal feature vector from the initially CAD-generated large feature pool and improve performance of machine learning model. We assemble a retrospective dataset involving 1,487 cases of mammograms in which 644 cases have confirmed malignant mass lesions and 843 have benign lesions. A CAD scheme is first applied to segment mass regions and initially compute 181 features. Then, support vector machine (SVM) models embedded with several feature dimensionality reduction methods are built to predict likelihood of lesions being malignant. All SVM models are trained and tested using a leave-one-case-out cross-validation method. SVM generates a likelihood score of each segmented mass region depicting on one-view mammogram. By fusion of two scores of the same mass depicting on two-view mammograms, a case-based likelihood score is also evaluated. Comparing with the principle component analyses, nonnegative matrix factorization, and Chi-squared methods, SVM embedded with RPA yielded a significantly higher case-based lesion classification performance with the area under ROC curve of 0.84 ± 0.01 (p<0.02). The study demonstrates that RPA is a promising method to generate optimal feature vectors and improve SVM performance. This study presents a new method to develop CAD schemes with significantly higher and robust performance.

Segmentation of teeth in panoramic dental X-ray images using U-Net with a loss function weighted on the tooth edge.

Panoramic dental X-ray imaging is an established method for the diagnosis of dental problems. However, the resolution of panoramic dental X-ray images is relatively low. Thus, early lesions are often overlooked. As the first step in the development of a computer-aided diagnosis scheme for panoramic dental X-ray images, we propose a computerized method for the segmentation of teeth using U-Net with a loss function weighted on the tooth edge. Our database consisted of 162 panoramic dental X-ray images. The training dataset consisted of 102 images, while the remaining 60 images were used as the test dataset. The loss function obtained by the cross entropy (CE) in the entire image is usually used in training U-Net. To improve the segmentation accuracy of the tooth edge, a loss function weighted on the tooth edge is proposed by adding the CE in the tooth edge region to the CE for the entire image. The mean Jaccard index and Dice index for U-Net with the loss function combining the CEs for the entire image and tooth edge were 0.864 and 0.927, respectively, which were significantly larger than those for U-Net with the CE for the entire image (0.802 and 0.890, p < 0.001) and U-Net with the CE for the tooth edge (0.826 and 0.905, p < 0.001). U-Net with the new loss function exhibited a higher segmentation accuracy of the tooth in panoramic dental X-ray images than that obtained by U-Net with the conventional loss function.

Breast cancer detection using active contour and classification by deep belief network

The abnormal modifications in tissues or cells of the body and growth beyond normal grow and control is called cancer. Breast cancer is one of the kinds of cancer. The existence level of patient suffering from breast cancer can be improved through Prognosis detection of breast cancer. The cancer diagnosis and detection accuracy have upgraded with the machine learning techniques, and the improvement of expertise. A computer-aided diagnosis (CAD) system is used for the mammograms to enable initial breast Cancer recognition, analysis, and treatment. For existing CAD system the accuracy is unacceptable. This research work explores a breast CAD scheme established on feature fusion through Convolution Neural Network deep features. The outcome shows that the random forest algorithm contributes the maximum accuracy (97.51%) with the lowest error rate than CNN classifier (95.65%). The abnormality of the breast images is scrutinized through Deep Belief Network (DBN). The offered work practices active contour segmentation to distinct the abnormal image and it can be categorized by Deep Belief Network. The recommended algorithm is used to eradicate the properties of the point spread function in the method of low-dose medical CT image restoration and recovers the reconstructed image quality. The efficiency and speed of the segmentation process are increased through sparse transform. Our mentioned method is used to afford the enhanced performance, offer a good implementation of the results, evaluated the accuracy and the processing time.