Gait-based Recognition for Human Identification using Fuzzy Local Binary Patterns

Binsaadoon, Amer G.; El-Alfy, El-Sayed M.

doi:10.5220/0005693103140321

Cited by 10 publications

(2 citation statements)

References 24 publications

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“…The vast majority of the classifiers require information marks for preparation; however it is over the top expensive for producing named information in radiology. All in all, a portion of the solo calculations are utilized to characterize unlabelled information [10]. Convolution Neural Networks (CNN) to take in the various levelled portrayals from the unlabelled pictures.…”

Section: Introductionmentioning

confidence: 99%

Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

Manickavasagam¹,

Sugumaran²

2023

Computer Systems Science and Engineering

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Cancer disease is a deadliest disease cause more dangerous one. By identifying the disease through Artificial intelligence to getting the mage features directly from patients. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification. This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy. To resolve this problem, to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor. The general technique of the created approach includes four stages, such as pre-processing, segmentation, highlight extraction, and the order. From the outset, the Computerized Tomography (CT) image of the lung is taken care of to the division. When the division is done, the highlights are extricated through morphological factors for feature observation. By getting the features are analysed and the characterization is done dependent on the Deep Belief Network (DBN) which is prepared by utilizing the proposed Chicken-Sine Cosine Algorithm (CSCA) which distinguish the lung tumour, giving two classes in particular, knob or non-knob. The proposed system produce high performance as well compared to the other system. The presentation assessment of lung knob division and malignant growth grouping dependent on CSCA is figured utilizing three measurements to be specificity, precision, affectability, and the explicitness.

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Section: Introductionmentioning

confidence: 99%

Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

Manickavasagam¹,

Sugumaran²

2023

Computer Systems Science and Engineering

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“…Then, the pre-processed image is fed to the feature extraction module in which the features, like CNN features, and local PPBTF are effectively extracted. Here, the local PPBTF is the combination of texture features and pixel pattern-based features [24], which is modified using LBP [25]. After extracting the features, the skin cancer detection mechanism is carried out with the proposed optimisation algorithm.…”

Section: Proposed Social Bat (Sb)-based Deep Stacked Auto-encoder Formentioning

confidence: 99%

Social bat optimisation dependent deep stacked auto‐encoder for skin cancer detection

Majji¹,

Prakash

Cristin³

et al. 2020

IET image process

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Nowadays, skin cancer is one of the most dangerous forms of cancer found in humans. There are various types of skin cancer, like basal, melanoma, carcinoma, and the squamous cell from which the melanoma is unpredictable. Thus, skin cancer detection in the early stage is very useful to treat it successfully. Hence, this study introduces a new algorithm called social bat optimisation algorithm for skin cancer detection. Initially, the pre-processing is done for the input image to eliminate the noise and artefacts present in the image. Then, the pre-processed image is fed to the feature extraction step where the features are extracted based on convolutional neural network features, and the local pixel pattern-based texture feature (local PPBTF). Here, the PPBTF is the combination of texture features and pixel pattern-based features in which the equation of PPBTF is modified based on the local binary pattern. Subsequently, the classification is done based on the extracted features using a deep stacked auto-encoder, which is trained by the proposed social bat optimisation. The performance of skin cancer detection based on the proposed model is evaluated based on accuracy, sensitivity, and specificity. The proposed model achieves the maximal accuracy of 93.38%, maximal sensitivity of 95%, and the maximal specificity of 96% for K-fold.

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