Automatic Kidney Lesion Detection for CT Images Using Morphological CNN

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Archana P
Chethan. S
Chiranth. M. V
Jeevan Reddy. K. N
Sanketha. Gowda. A

Abstract

The CT scan is the best tool for diagnosing and finding injuries in the kidney. It can provide precise information about the location and size of lesions in many medical applications. Manual and traditional medical tests work and time-consuming. The automatic detection of injuries in CT is now an integral task for clinical diagnosis. To develop and improve the efficiency of medical testing computer-aided diagnosis (CAD) is needed. However, the existing low accuracy and incomplete detection algorithm remain a tremendous challenge. The proposed lesion sensor is based on morphological cascaded convolutional neural networks using a multi-intersection threshold (IOU) (CNNs). To increase network stability and morphology co-detection layers and amended pyramid networks in the faster RCNN and combine four IOU threshing thresholds with cascade RCNNs and for better detection of small lesions (1-5 mm). In addition, the experiments have been conducted on CT deep-lesion kidney pictures published by photos and communication systems of hospitals (PACSs

 

 

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M. Shehata, F. Khalifa, A. Soliman, M. Ghazal, F. Taher, M. A. El-Ghar, A. C. Dwyer, G. Gimel'farb, R. S. Keynton, and A. El-Baz, “Computeraided diagnostic system for early detection of acute renal transplant rejection using diffusion-weighted MRI,†IEEE Trans. Biomed. Eng., vol. 66, no. 2, Feb. 2019

J. Jiang, P. Trundle, and J. Ren, “Medical image analysis with artifcial neural networks,†Comput. Med. Imag. Graph., vol. 34, no. 8, Dec. 2010

M. Noll, X. Li, and S.Wesarg, “Automated kidney detection and segmentation in 3D ultrasoundâ€, in Proc. Workshop Clin. Image-Based Procedures, Sep. 2014,

W. K. Moon, Y.-W. Shen, M. S. Bae, C.-S. Huang, J.-H. Chen, and R.-F. Chang, “Computer-aided tumor detection based on multi-scale blob detection algorithm in automated breast ultrasound imagesâ€, IEEE Trans. Med. Imag., vol. 32, no. 7, Jul. 2013

Y. Li, “Detecting lesion bounding ellipses with Gaussian proposal networksâ€, 2018.

R. Cuingnet, R. Prevost, D. Lesage, L. D. Cohen, B. Mory, and R Ardon, “Automatic detection and segmentation of kidneys in 3D CT images using random forestsâ€, in Proc. Int. Conf. Med. Image Comput. Comput. Assist. Intervent., Oct. 2012.

M. Zhang, T. Wu, S. C. Beeman, L. Cullen-McEwen, J. F. Bertram, J. R. Charlto, E. Baldelomar, and K. M. Bennett, “Effcient small blob detection based on local convexity, intensity and shape informationâ€, IEEE Trans. Med. Imag., vol. 35, no. 4

J. Zhou and J. Qi, ``Adaptive imaging for lesion detection using a zoomin PET system,'' IEEE Trans. Med. Imag., vol. 30, no. 1, Jan. 2011.

Ashwinkumar.U.M and Dr. Anandakumar K.R, "Predicting Early Detection of cardiac and Diabetes symptoms using Data mining techniques", International conference on computer Design and Engineering, vol.49, 2012.

A. Ben-Cohen, E. Klang, A. Kerpel, E. Konen, M. M. Amitai, and H. Greenspan, ``Fully convolutional network and sparsity-based dictionary learning for liver lesion detection in CT examinations,'' Neurocomputing, vol. 275, Jan. 2018.

K. Yan, X. Wang, L. Lu, and R. M. Summers, ``DeepLesion: Automated mining of large-scale lesion annotations and universal lesion detection with deep learning,'' Proc. SPIE, vol. 5, no. 3, Jul. 2018, Art. no. 03650