Automatic Computer-aided Detection System Research Articles

Automatic lung parenchyma segmentation using a deep convolutional neural network from chest X-rays

To detect and diagnosis the lungs related diseases, a Chest X-Ray (CXR) is the major tool used by the physician. Automated organ segmentation contributes to a crucial part of computer-aided detection (CAD) and diagnosis of diseases from CXRs as they enhance the accuracy in detecting, and also help in reducing the burden raised due to manual diagnosis from radiologists and medical practitioners. In this paper, an efficient automatic CAD system is proposed to detect the boundaries using a deep convolutional neural network (DCNN) model. The DCNN is trained in an end-to-end setting to facilitate fully automatic lung segmentation from anteroposterior or posteroanterior view CXRs. It learns to predict binary masks for a given CXR, by learning to discriminate regions of organ parenchyma from regions of no organ parenchyma. The proposed model’s architecture makes use of residual connections in all the concurrent up-sampling paths from each encoder block at every level, thus facilitating collective learning within blocks through inter-sharing of all high-dimensional feature maps. To generalize the proposed model to CXRs from all data distributions, image preprocessing techniques such as Top-Hat Bottom-Hat Transform and Contrast Limited Adaptive Histogram Equalization are employed. The proposed model is trained and tested using the JSRT, NLM-MC and Shenzhen Hospital datasets. The proposed method achieved a Dice Similarity Coefficient of 0.982 ± 0.018 and a Jaccard Similarity Coefficient of 0.967 ± 0.015. The implementation results demonstrated that the proposed method has surpassed the existing methods and our model is relatively lightweight and can be easily implemented on standard GPUs.

Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy.

Evidence has shown that deep learning computer aided detection (CADe) system achieved high overall detection accuracy for polyp detection during colonoscopy. The detection performance of CADe system on non-polypoid laterally spreading tumors (LSTs) and sessile serrated adenomas/polyps (SSA/Ps), with higher risk for malignancy transformation and miss rate, has not been exclusively investigated. A previously validated deep learning CADe system for polyp detection was tested exclusively on LSTs and SSA/Ps. 1451 LST images from 184 patients were collected between July 2015 and January 2019, 82 SSA/Ps videos from 26 patients were collected between September 2018 and January 2019. The per-frame sensitivity and per-lesion sensitivity were calculated. (1) For LSTs image dataset, the system achieved an overall per-image sensitivity and per-lesion sensitivity of 94.07% (1365/1451) and 98.99% (197/199) respectively. The per-frame sensitivity for LST-G(H), LST-G(M), LST-NG(F), LST-NG(PD) was 93.97% (343/365), 98.72% (692/701), 85.71% (324/378) and 85.71% (6/7) respectively. The per-lesion sensitivity of each subgroup was 100.00% (71/71), 100.00% (64/64), 98.31% (58/59) and 80.00% (4/5). (2) For SSA/Ps video dataset, the system achieved an overall per-frame sensitivity and per-lesion sensitivity of 84.10% (15883/18885) and 100.00% (42/42), respectively. This study demonstrated that a local-feature-prioritized automatic CADe system could detect LSTs and SSA/Ps with high sensitivity. The per-frame sensitivity for non-granular LSTs and small SSA/Ps should be further improved.

Automatic Detection of Lung Cancer Nodules in Computerized Tomography Images

Lung cancer is the prime factor in cancer related deaths due to increasing rate of smoking and air pollution. The survival rate of cancer patients increases to 52% if it is localized, and decreases to 4% if it is metastasized. The existing system use simple thresholding approach and pattern recognition method to segment lung and identify nodules in the lung computerized tomography (CT) images. But quality of this process is affected by the image acquisition protocols, scanner types, and inhomogeneity of intensities in the lung region. In order to overcome the drawbacks of the previous process and for separation of nodules from non-nodules, there is a need for an automatic computer aided detection system. This paper proposes an automatic method for detecting nodules from patient lung CT images. In this automatic approach first step is a lung segmentation to differentiate lung and background and make the detection of nodule simple. Then the lung region is segmented by Contextual Clustering based region growing method. Vector quantization method is used to identify the nodules present in the lungs. False positive reduction is done by using various combination of rule based filtering, support vector machine, k-Nearest Neighbours and Random Forest classifier. The performance of this method is evaluated in terms of accuracy, sensitivity and specificity.

Efficacy of automated computer-aided diagnosis of retinal nerve fibre layer defects in healthcare screening.

To evaluate the efficacy of a new automatic computer-aided detection (CAD) system for mass screening of retinal nerve fibre layer (RNFL) defects in a large population using fundus photographs. Among the fundus photographs of 1200 consecutive subjects who visited a healthcare centre, a total of 2270 photographs appropriate for analysis were tested. The photographs were first reviewed by two expert ophthalmologists for detection of RNFL defects (gold standard manual detection). The images were then analysed using an automatic CAD system for detecting RNFL defects in various cases of glaucomatous and non-glaucomatous optic neuropathy. A free-response receiver operating characteristics curve was generated to evaluate the validity of the CAD system. The results of the automatic detection were compared with those of manual detection, and sensitivity and specificity of the CAD system were calculated. In manual detection of 2270 photographs, 41 RNFL defects from 36 photographs (1.6%) were detected, and no RNFL defects were found in 2234 photographs (98.4%). The sensitivity of the CAD system for detecting RNFL defects was 90.2% (37/41 RNFL defects) and the specificity was 72.5% (1620/2234 photographs with no RNFL defects) at a false-positive rate of 0.36 per image. The new CAD system successfully detected RNFL defects during mass screening of fundus photographs in a large population who visited a healthcare centre. The proposed algorithm can be useful for clinicians in screening RNFL defects in healthcare centres. The false-positive rate is still unsatisfactory, although improved compared with the previous study. Further studies are needed to enhance the speed and specificity of image analysis using the CAD system.

Automatic Detection of Exudates in Color Fundus Retinopathy Images

Objective: The aim of this paper is to design a computationally intelligent method to determine exudates, the Non-Proliferative Diabetic Retinopathy (NPDR) symptom which is considered to be the initial stage of retinopathy disease. If NPDR is not identified at its earlier stage, it may lead to Proliferative Diabetic Retinopathy (DR), the complicated stage of retinal symptom that may leads to blindness. Methods: It is proposed to develop an automatic computer aided detection system that screen a large number of people to identify the DR in its earlier stage for proper treatments. In this work, images are taken from publicly available e-optha database. Analysis mainly considers three stages which include removal of optic disc and normalization done by histogram processing; texture information extracted using Gray Level Co-Occurrence Matrix (GLCM) and classification is done with the help of Support Vector Machines (SVM). Findings: Preprocessing method used in this work enhances the contrast of the low or poor quality images. Here, optic disc segmentation is performed, which helps in providing better result by avoiding the misclassification of optic disc as lesions. GLCM method provides different texture features to SVM o provide better result in identifying exudate lesions. Conclusion: The intelligent machine learning approaches aid the ophthalmologists with accurate and efficient detection of abnormalities in fundus images. Through this system, the abnormal retinal images can be identified in its initial stage and an accurate assessment of retinal disease is possible.