scholarly journals AN INTELLIGENT CBMIR SYSTEM FOR DETECTION AND LOCALIZATION OF LUNG DISEASES

2021 ◽  
Vol 9 (08) ◽  
pp. 651-660
Author(s):  
Nora I. Yahia ◽  
◽  
Ayman I. Al-Dosouki ◽  
Sahar A. Mokhtar ◽  
Hany M. Harb ◽  
...  
Keyword(s):  
Medical Image ◽  
Lung Diseases ◽  
Medical Instrument ◽  
Detection Accuracy ◽  
Retrieval Process ◽  
Recognition Efficiency ◽  
Imaging Signs ◽  
Detection And Localization ◽  
Aided Diagnosis ◽  
Detector Model

The diagnosis of lung diseases is a complicated and time-consuming task for radiologists. Often radiologists struggle with accurately diagnosing lung diseases, They use Commonly CT imaging signs (CISs) which common appear in CT lung nodules in the diagnosis of lung diseases. Computer-aided diagnosis systems (CAD) can automatically diagnose and detect these signs by analyzing CT scans, which will reduce radiologists workload. The diagnosis and recognition efficiency and accuracy can be improved by using content-based medical image retrieval (CBMIR). This paper proposes a new intelligent CBMIR method to retrieve CISs helping in diagnosing and recognize lung diseases by using deep Convolutional Neural Network (CNN). Fine-tuned YOLOv4 (You Only Look Once) object detector model are proposed to fast detect and efficiently localize signs in real-time. The proposed CBMIR system can be applied as a useful and accurate medical instrument for diagnostics. The experimental results show an average detection accuracy of CT signs lung diseases as high as 92% and a mean average precision (MAP) of 0.92 is achieved using the proposed technique. Also, it takes only 0.1 milliseconds for the retrieval process. The proposed system presents high improvement as compared to the other system. It achieved better precision of retrieval results and the fastest run of the retrieval time.

scholarly journals An effective approach for CT lung segmentation using region growing

2021 ◽  
Vol 2082 (1) ◽  
pp. 012001
Author(s):  
Xi Yang ◽  
Guanyu Xu ◽  
Teng Zhou
Keyword(s):  
Medical Image ◽  
Lung Diseases ◽  
Region Growing ◽  
Computer Aided Diagnosis ◽  
Medical Image Segmentation ◽  
Point Selection ◽  
Seed Point ◽  
Lung Segmentation ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract X-ray is an important means of detecting lung diseases. With the increasing incidence of lung diseases, computer-aided diagnosis technology is of great significance in clinical treatment. It has become a hot research direction to use computer-aided diagnosis to recognize chest radiography images, which can alleviate the uneven status of regional medical level. For clinical diagnosis, medical image segmentation can enable users to timely obtain the target region they are interested in and analyze it, which is significant to be used as an important basis for auxiliary research and judgment. In this case, a region growing algorithm based on threshold presegmentation is selected for lung segmentation, which integrates image enhancement, threshold segmentation, seed point selection and morphological post-processing, etc., to improve the segmentation effect, which also has certain reference value for other medical image processing.

scholarly journals CUSTOMIZED CONVOLUTION NEURAL NETWORK FOR MULTI-CLASS LUNG ABNORMALITY CLASSIFICATION FROM CT IMAGES

2021 ◽  
Vol 9 (1) ◽  
pp. 49-57
Author(s):  
D. Lakshmi, J. Sivakumar, K. Palani Thanaraj, N. Thendral
Keyword(s):  
Neural Network ◽  
Medical Image ◽  
Lung Diseases ◽  
Medical Image Analysis ◽  
Specific Treatment ◽  
Ct Images ◽  
Automated Detection ◽  
Convolution Neural Network ◽  
Lung Abnormalities ◽  
Aided Diagnosis

Automated detection of lung abnormalities has a significant role in the computer aided diagnosis of lung diseases.  Recently, medical image analysis utilizes Convolution Neural Network(CNN) to improve the outcome of clinical diagnosis.  In this paper, we propose customized CNN based multi-class lung abnormality classifier from CT images.  The custom CNN is trained and tested using CT images showing lung abnormalities of  Carcinoma, Fibrosis, Necrosis and their performance  is also compared with the results using VGG16 and VGG19.  It is found that the our Custom CNN shows good results for Carcinoma, Fibrosis, Healthy, Inflammation and Necrosis with classification accuracy of 0.912 compared to VGG16 and VGG19 with accuracy of 0.7435 and 0.7216 respectively.  Hence, it is proven that our custom CNN can be utilized as a second opinion to radiologist expert and improving mortality rate of these lung diseases by providing class-specific treatment for the patients.

scholarly journals MAGAN: Mask Attention Generative Adversarial Network for Liver Tumor CT Image Synthesis

2020 ◽  
Author(s):  
Yang Liu ◽  
Lu Meng ◽  
Jianping Zhong
Keyword(s):  
Liver Tumor ◽  
Medical Image ◽  
Ct Images ◽  
Computer Aided Diagnosis ◽  
Generative Adversarial Network ◽  
Liver Ct ◽  
Adversarial Network ◽  
Computer Aided ◽  
Image Dataset ◽  
Aided Diagnosis

Abstract Background: For deep learning, the size of the dataset greatly affects the final training effect. However, in the field of computer-aided diagnosis, medical image datasets are often limited and even scarce.Methods: We aim to synthesize medical images and enlarge the size of the medical image dataset. In the present study, we synthesized the liver CT images with a tumor based on the mask attention generative adversarial network (MAGAN). We masked the pixels of the liver tumor in the image as the attention map. And both the original image and attention map were loaded into the generator network to obtain the synthesized images. Then the original images, the attention map, and the synthesized images were all loaded into the discriminator network to determine if the synthesized images were real or fake. Finally, we can use the generator network to synthesize liver CT images with a tumor.Results: The experiments showed that our method outperformed the other state-of-the-art methods, and can achieve a mean peak signal-to-noise ratio (PSNR) as 64.72dB.Conclusions: All these results indicated that our method can synthesize liver CT images with tumor, and build large medical image dataset, which may facilitate the progress of medical image analysis and computer-aided diagnosis.

scholarly journals SKIN LESION CLASSIFICATION FROM DERMOSCOPY AND CLINICAL IMAGES WITH A DEEP LEARNING APPROACH

2021 ◽  
Vol 9 (10) ◽  
pp. 1294-1300
Author(s):  
Aigli Korfiati ◽  
◽  
Giorgos Livanos ◽  
Christos Konstandinou ◽  
Sophia Georgiou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Medical Image ◽  
Skin Lesions ◽  
Learning Approaches ◽  
Early Screening ◽  
Clinical Images ◽  
Cad Systems ◽  
Learning Classifier ◽  
Aided Diagnosis

Computer-aided diagnosis (CAD) systems based on deep learning approaches are now feasible due to the availability of big data and the availability of powerful computational resources.The medical image-based CAD systems are of great interest in numerous diseases, but especially for skin cancer diagnosis, deep learning models have been mostly developed for dermoscopy images. Models for clinical images are few, mainly due to the unavailability of big volumes of relevant data. However, CAD systems able to classify skin lesions from clinical images would be of great valueboth for the population and clinicians as an initial early screening of lesions that would leadpatients to visiting a dermatologist in case of suspicious lesions. This is even more pronounced in areas where there is lack of dermoscopy instruments. Thus, in this paper, we aimed to build a classifier based on bothdermoscopy and clinical images able to discriminate skin cancer from skin lesions. The classification is made among three benign and two malignant categories, which include Nevus, Benign but not nevus, Benign but suspicious for malignancy, Melanoma and Non-Melanocytic Carcinoma.The proposed deep learning classifier achieves an Area Under Curve ranging between 0.75 and 0.9 for the five examined categories.

scholarly journals A Novel Computer-Aided Diagnosis Scheme on Small Annotated Set: G2C-CAD

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Guangyuan Zheng ◽  
Guanghui Han ◽  
Nouman Q. Soomro ◽  
Linjuan Ma ◽  
Fuquan Zhang ◽  
...  
Keyword(s):  
Medical Image ◽  
Medical Image Analysis ◽  
Ground Glass Opacity ◽  
Semisupervised Learning ◽  
Small Sample ◽  
Negative Image ◽  
Cad System ◽  
Computer Aided ◽  
Sample Set ◽  
Aided Diagnosis

Purpose. Computer-aided diagnosis (CAD) can aid in improving diagnostic level; however, the main problem currently faced by CAD is that it cannot obtain sufficient labeled samples. To solve this problem, in this study, we adopt a generative adversarial network (GAN) approach and design a semisupervised learning algorithm, named G2C-CAD. Methods. From the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, we extracted four types of pulmonary nodule sign images closely related to lung cancer: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, obtaining a total of 3,196 samples. In addition, we randomly selected 2,000 non-lesion image blocks as negative samples. We split the data 90% for training and 10% for testing. We designed a DCGAN generative adversarial framework and trained it on the small sample set. We also trained our designed CNN-based fuzzy Co-forest on the labeled small sample set and obtained a preliminary classifier. Then, coupled with the simulated unlabeled samples generated by the trained DCGAN, we conducted iterative semisupervised learning, which continually improved the classification performance of the fuzzy Co-forest until the termination condition was reached. Finally, we tested the fuzzy Co-forest and compared its performance with that of a C4.5 random decision forest and the G2C-CAD system without the fuzzy scheme, using ROC and confusion matrix for evaluation. Results. Four different types of lung cancer-related signs were used in the classification experiment: noncentral calcification, lobulation, spiculation, and nonsolid/ground-glass opacity (GGO) texture, along with negative image samples. For these five classes, the G2C-CAD system obtained AUCs of 0.946, 0.912, 0.908, 0.887, and 0.939, respectively. The average accuracy of G2C-CAD exceeded that of the C4.5 random decision tree by 14%. G2C-CAD also obtained promising test results on the LISS signs dataset; its AUCs for GGO, lobulation, spiculation, pleural indentation, and negative image samples were 0.972, 0.964, 0.941, 0.967, and 0.953, respectively. Conclusion. The experimental results show that G2C-CAD is an appropriate method for addressing the problem of insufficient labeled samples in the medical image analysis field. Moreover, our system can be used to establish a training sample library for CAD classification diagnosis, which is important for future medical image analysis.

scholarly journals Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis

2020 ◽  
Vol 117 (23) ◽  
pp. 12592-12594 ◽  
Author(s):  
Agostina J. Larrazabal ◽  
Nicolás Nieto ◽  
Victoria Peterson ◽  
Diego H. Milone ◽  
Enzo Ferrante
Keyword(s):  
Medical Imaging ◽  
Medical Image ◽  
Computer Aided Diagnosis ◽  
Computer Assisted ◽  
Gender Imbalance ◽  
Gender Balance ◽  
Computer Aided ◽  
Medical Image Computing ◽  
Assisted Diagnosis ◽  
Aided Diagnosis

Artificial intelligence (AI) systems for computer-aided diagnosis and image-based screening are being adopted worldwide by medical institutions. In such a context, generating fair and unbiased classifiers becomes of paramount importance. The research community of medical image computing is making great efforts in developing more accurate algorithms to assist medical doctors in the difficult task of disease diagnosis. However, little attention is paid to the way databases are collected and how this may influence the performance of AI systems. Our study sheds light on the importance of gender balance in medical imaging datasets used to train AI systems for computer-assisted diagnosis. We provide empirical evidence supported by a large-scale study, based on three deep neural network architectures and two well-known publicly available X-ray image datasets used to diagnose various thoracic diseases under different gender imbalance conditions. We found a consistent decrease in performance for underrepresented genders when a minimum balance is not fulfilled. This raises the alarm for national agencies in charge of regulating and approving computer-assisted diagnosis systems, which should include explicit gender balance and diversity recommendations. We also establish an open problem for the academic medical image computing community which needs to be addressed by novel algorithms endowed with robustness to gender imbalance.

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