Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network

Wang, Yu; Chen, Yating; Yang, Ningning; Zheng, Lihua; Dey, Nilanjan; Ashour, Amira S.; Rajinikanth, V.; Tavares, João Manuel R. S.; Shi, Fuqian

doi:10.1016/j.asoc.2018.10.006

Cited by 54 publications

(19 citation statements)

References 34 publications

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“…In future, a feature extraction and classification procedure for the extracted tumor section can be implemented to classify the brain tumors, such as benign, and malignant. Further, the adopted brain MRI images can be examined using the deep-learning procedures based on the Neural Network (NN) [49][50][51] and its outcome can be compared and validated with the machine-learning procedure discussed in this work.…”

Section: Resultsmentioning

confidence: 99%

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Satapathy

Rajinikanth

2018

Journal of Optimization

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Brain abnormality is a cause for the chief risk factors in human society with larger morbidity rate. Identification of tumor in its early stage is essential to provide necessary treatment procedure to save the patient. In this work, Jaya Algorithm (JA) and Otsu’s Function (OF) guided method is presented to mine the irregular section of brain MRI recorded with Flair and T2 modality. This work implements a two-step process to examine the brain tumor from the axial, sagittal, and coronal views of the two-dimensional (2D) MRI slices. This paper presents a detailed evaluation of thresholding procedure with varied threshold levels (Th=2,3,4,5), skull stripping process before/after the thresholding practice, and the tumor extraction based on the Chan-Vese approach. Superiority of JA is confirmed among other prominent heuristic approaches found in literature. The outcome of implemented study confirms that Jaya Algorithm guided method is capable of presenting superior values of Jaccard-Index, Dice-Coefficient, sensitivity, specificity, accuracy, and precision on the BRATS 2015 dataset.

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Section: Resultsmentioning

confidence: 99%

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Satapathy

Rajinikanth

2018

Journal of Optimization

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“…Unlike previous trackers, more emphasis is put on unsupervised feature learning. A noteworthy performance improvement in visual tracking is observed with the introduction of deep neural networks (DNN) [269,270] and convolutional neural networks (CNN) [271][272][273][274][275]. DNN, especially CNN, demonstrate a strong efficiency in learning feature representations from huge annotated visual data unlike handcrafted features.…”

Section: Discussionmentioning

confidence: 99%

Vision Tracking: A Survey of the State-of-the-Art

et al. 2020

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Vision tracking is a well-studied framework in vision computing. Developing a robust visual tracking system is challenging because of the sudden change in object motion, cluttered background, partial occlusion and camera motion. In this study, the state-of-the art visual tracking methods are reviewed and different categories are discussed. The overall visual tracking process is divided into four stages-object initialization, appearance modeling, motion estimation, and object localization. Each of these stages is briefly elaborated and related researches are discussed. A rapid growth of visual tracking algorithms is observed in last few decades. A comprehensive review is reported on different performance metrics to evaluate the efficiency of visual tracking algorithms which might help researchers to identify new avenues in this area. Various application areas of the visual tracking are also discussed at the end of the study.

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“…Thus, F(a, b|d, θ) is defined that the emergence probability of the certain pair pixels which locate in the image, shown as (a, b)|a = f (x, y)&b = f (x + dx, y + dy). Texture features extracted from the GLCM like contrast, correlation, dissimilarity, variance, and entropy [20] are employed. In the infrared thermal image, the temperature changes of the objects in the scene are collectively reflected, and the gray level distribution is concentrated, the range is narrow.…”

Section: Machine Learning Techniques Used Texture Features 1) Graymentioning

confidence: 99%

Infrared Thermal Images Classification for Pressure Injury Prevention Incorporating the Convolutional Neural Networks

Wang

Jiang

et al. 2021

IEEE Access

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Hospital-acquired pressure injury is difficult to identify in the early stage, accompanied with increased morbidity but considered to be preventable. For helping the nurses to monitor the status of the patients' skin, the infrared thermal imaging and the convolutional neural networks were integrated to identify and prevent pressure injury. In the first stage, infrared thermal images were shoot and labelled with the normal group and the pressure injury group by the clinical nurses. In the second stage, the convolutional neural networks and two machine learning algorithms, the random forest and the support vector machine, were applied to classify these two classes of the collected images. The classification model was trained on 164 images and was tested on the special image dataset consisted of 82 infrared thermal images of 1 day before pressure injury. Gray level co-occurrence matrix was utilized to extract the texture features of the infrared thermal images and we chose the pearson correlation coefficient and the Chi square test as the feature selection methods. The classification accuracy of the proposed convolutional neural networks model was 95.2% and the area under curve was 0.98. Moreover, the classification results from the test dataset were conformed to the experience of the experts. After feature selection, variance and entropy were proved to the best distinguishable features. Finally, we concluded that combining the infrared thermal imaging and convolutional neural networks could contribute to the prevention of pressure injury. This measure should be performed in high-risk populations to reduce the incidence of pressure injury.INDEX TERMS Convolutional neural networks, thermal image, pressure injury.

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Classification of mice hepatic granuloma microscopic images based on a deep convolutional neural network

Cited by 54 publications

References 34 publications

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Jaya Algorithm Guided Procedure to Segment Tumor from Brain MRI

Vision Tracking: A Survey of the State-of-the-Art

Infrared Thermal Images Classification for Pressure Injury Prevention Incorporating the Convolutional Neural Networks

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