A Professional Analysis and Evaluation of Computed Tomography Brain Tumor Images using SDNN for Segmentation and SOM-LS-SVM for Classification

Ramakrishnan, Thiagarajan; Sankaragomathi, B.

doi:10.1166/jmihi.2016.1822

Cited by 3 publications

(1 citation statement)

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“…Medical image is of great value to the diagnosis and treatment of diseases. Image classification can get disease performance types from massive data, and classification of a certain part can also provide an important role in disease prediction [ 20 ]. At present, computer-aided technology has been applied in medical image classification, and semiautomatic and full-automatic classification and diagnosis methods are the key topics of medical research [ 21 ].…”

Section: Related Wordmentioning

confidence: 99%

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

2022

Computational and Mathematical Methods in Medicine

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Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.

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Section: Related Wordmentioning

confidence: 99%

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

2022

Computational and Mathematical Methods in Medicine

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Medical image segmentation algorithm based on multilayer boundary perception-self attention deep learning model

Liu

2021

Multimed Tools Appl

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Features Extraction to Differentiate of Spinal Curvature Types using Hue Moment Algorithm

Salleh

Kanafiah

Jusman

et al. 2020

J. Phys.: Conf. Ser.

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Nowadays, diagnosing the spinal problems is very important to medical field. The objective of this research is to develop feature extraction technique to obtain the features, which automatically differentiate images of normal and abnormal (scoliosis) spinal curvatures. The process to extract features of spinal image start with image acquisition, image processing (i.e. enhancement, filtering, and segmentation). For image processing method, the most important part in this phase is the segmentation using manual threshold method. After the segmentation, hue moment for size and parameter are used to extract features that should be considered based on probabilistic to classify the spine images. The final experimental result shows that the developed features extraction technique can differentiate between normal and scoliosis spine images.

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A Professional Analysis and Evaluation of Computed Tomography Brain Tumor Images using SDNN for Segmentation and SOM-LS-SVM for Classification

Cited by 3 publications

References 0 publications

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

Medical image segmentation algorithm based on multilayer boundary perception-self attention deep learning model

Features Extraction to Differentiate of Spinal Curvature Types using Hue Moment Algorithm

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