Statistical Modeling based Directional Pattern Design (SMDPD) feature extraction for coral reef classification

Paul, Manoranjan; Rani, P. Arockia Jansi

doi:10.1007/s10661-021-09314-5

Cited by 13 publications

(3 citation statements)

References 35 publications

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“…At present, deep learning-based classification techniques are used widely. 42 According to the statistics, female breast cancer has risen to the top of the list of cancers. Zhu et al 43 analyzed a wide range of recent papers and provide a thorough assessment of the convolutional neural network-based diagnosis of breast cancer.…”

Section: The Classification Approachmentioning

confidence: 99%

“…The extracted features of the proposed DJAM are discussed in the previous Section 3.3, which is described in Dataset is fed into the classification process. At present, deep learning‐based classification techniques are used widely 42 . According to the statistics, female breast cancer has risen to the top of the list of cancers.…”

Section: Proposed System's Frameworkmentioning

confidence: 99%

“…3.3which is described in Dataset is fed into the classification process. At present, deep learning-based classification techniques are used widely [39]. Initially, an attempt was made to use deep learning classification technique in this work but it produce poor classification accuracy.…”

Section: The Approach Of Classificationmentioning

confidence: 99%

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An exploration of ventricle regions segmentation and multiclass disease detection using cardiacMRI

Velappan

Gomathi

Boopathi

2023

Int J Imaging Syst Tech

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In this modern era, various cardiac diseases are very crucial as they cause a high mortality rate. Early detection of cardio vascular disease (CVD) is essential to prevent and control it. Diagnosis of cardiac disease is the process of analyzing the left and right ventricle cavities (LV, RV) and myocardium (MYO) from cardiac magnetic resonance (CMR) images. As deep learning architectures are becoming more mature, segmenting, and classifying cardiac MRI images using deep learning is gaining more attention. This work is aimed to identify five different cardiac disease subgroups namely NOR, MINF, DCM, HCM, and ARC by employing a new deep join attention model (DJAM) technique for segmenting LV, MYO, and RV regions separately. This method provides advancement as the joined attention model was combined with the pooling layers and the resultant is added to the convolution layers. The proposed region integrated deep residual network (RIDRN) is used to extract the features from the segmented images for classification. In this process, the features of LV, RV, and MYO are combined with a different combination. The advantage of doing this process is to get the overall features without leaving any single strip of features from the three regions. Hence, it shows a rise in performance accuracy. The random forest classification method is used to classify the underlying features for cardiac disease diagnosis. This proposed work is tested in the automated cardiac diagnosis challenge (ACDC) dataset and it perfects the state‐of‐art techniques.

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Section: The Classification Approachmentioning

confidence: 99%

Section: Proposed System's Frameworkmentioning

confidence: 99%

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An exploration of ventricle regions segmentation and multiclass disease detection using cardiacMRI

Velappan

Gomathi

Boopathi

2023

Int J Imaging Syst Tech

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Computational Analysis and Performance Investigation of Convolutional Neural Network-Based Algorithms for Effective Face Spoof Detection

Amuthavalli¹,

Kumari²

2023

Lecture Notes in Networks and Systems

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LWDS: lightweight DeepSeagrass technique for classifying seagrass from underwater images

Paul¹,

Kumar

Sagar

et al. 2023

Environ Monit Assess

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In many coastal areas around the world, the seagrasses provide an essential source of livelihood for many civilizations and support high levels of biodiversity. Seagrasses are highly valuable, as they provide habitat for numerous sh, endangered sea cows, Dugong dugons, and sea turtles. The health of seagrasses is being threatened by many human activities. The process of seagrass conservation requires the annotation of every seagrass species within the seagrass family. The manual annotation procedure is time-consuming and lacks objectivity and uniformity. Automatic annotation based on Lightweight Deep Seagrass (LWDS) is proposed to solve this problem. LWDS computes combinations of various resized input images and various neural network structures, to determine the ideal reduced image size and neural network structure with satisfactory accuracy and within a reasonable computation time. The main advantage of this LWDS is it classi es the seagrasses quickly and with lesser parameters. The deepseagrass dataset is used to test LWDS's applicability.

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Statistical Modeling based Directional Pattern Design (SMDPD) feature extraction for coral reef classification

Cited by 13 publications

References 35 publications

An exploration of ventricle regions segmentation and multiclass disease detection using cardiacMRI

An exploration of ventricle regions segmentation and multiclass disease detection using cardiacMRI

Computational Analysis and Performance Investigation of Convolutional Neural Network-Based Algorithms for Effective Face Spoof Detection

LWDS: lightweight DeepSeagrass technique for classifying seagrass from underwater images

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