Analysis of Feature Extraction and Classification Models for Lip-Reading

Mestri, Rohan; Limaye, Prathamesh; Khuteta, Sagar; Bansode, Manisha

doi:10.1109/icoei.2019.8862649

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…[26][27][28] and relevant characteristics from segmented regions. [29][30][31] The goal of feature extraction is to minimize the dimensionality of the data so that it can be processed more easily and quickly while maintaining as much information as possible. Feature selection involves the process of selecting the most relevant and informative characteristics from a vast set of features derived from medical images.…”

Section: Conventional Cadx Systemsmentioning

confidence: 99%

Machine Learning Approaches for Skin Neoplasm Diagnosis

Asaduzzaman,

Thompson,

Uddin

2024

Preprint

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Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning has promises to effectively classify skin images into different classes such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original dataset for preventing model overfitting. Synthetic minority over-sampling technique (SMOTE) is employed to rectify class imbalances in the original dataset. To accurately classify skin images, the following four machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN-SVM. Experimental results using the HAM10000 dataset demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN-SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN, EDA, and SMOTE, the LDA, SVM, CNN, and ensemble CNN-SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and datasets in our next endeavor.

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Section: Conventional Cadx Systemsmentioning

confidence: 99%

Machine Learning Approaches for Skin Neoplasm Diagnosis

Asaduzzaman,

Thompson,

Uddin

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Feature extraction and selection, two important steps, are usually grouped in CADx systems. Feature extraction involves the technique of detecting and extracting the most critical and relevant characteristics from segmented regions. − The goal of feature extraction is to minimize the dimensionality of the data so that it can be processed more easily and quickly while maintaining as much information as possible. Feature selection involves the process of selecting the most relevant and informative characteristics from a vast set of features derived from medical images. − The purpose is to minimize the dimensionality of the feature space and remove redundant characteristics that might affect CADx system performance.…”

Section: Related Workmentioning

confidence: 99%

“…Feature extraction involves the technique of detecting and extracting the most critical and relevant characteristics from segmented regions. 29 − 31 The goal of feature extraction is to minimize the dimensionality of the data so that it can be processed more easily and quickly while maintaining as much information as possible. Feature selection involves the process of selecting the most relevant and informative characteristics from a vast set of features derived from medical images.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Approaches for Skin Neoplasm Diagnosis

Asaduzzaman,

Thompson,

Uddin

2024

ACS Omega

View full text Add to dashboard Cite

Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning shows promise in effectively classifying skin images into different categories such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) discriminator is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original data set for preventing model overfitting. Synthetic minority oversampling technique (SMOTE) is employed to rectify class imbalances in the original data set. To accurately classify skin images, the following machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN−SVM. Experimental results using the HAM10000 data set demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN−SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN (discriminator) and SMOTE, the LDA, SVM, CNN, and ensemble CNN−SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and data sets in our next endeavor.

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“…Furthermore, people can talk from different angles towards a camera. Because of these issues, it is essential to create more robust models [6].…”

Section: Introductionmentioning

confidence: 99%

Developing phoneme‐based lip‐reading sentences system for silent speech recognition

El-Bialy

Chen

Fenghour

et al. 2022

CAAI Trans on Intel Tech

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Lip-reading is a process of interpreting speech by visually analysing lip movements. Recent research in this area has shifted from simple word recognition to lip-reading sentences in the wild. This paper attempts to use phonemes as a classification schema for lip-reading sentences to explore an alternative schema and to enhance system performance. Different classification schemas have been investigated, including characterbased and visemes-based schemas. The visual front-end model of the system consists of a Spatial-Temporal (3D) convolution followed by a 2D ResNet. Transformers utilise multi-headed attention for phoneme recognition models. For the language model, a Recurrent Neural Network is used. The performance of the proposed system has been testified with the BBC Lip Reading Sentences 2 (LRS2) benchmark dataset. Compared with the state-of-the-art approaches in lip-reading sentences, the proposed system has demonstrated an improved performance by a 10% lower word error rate on average under varying illumination ratios. K E Y W O R D S deep learning, deep neural networks, lip-reading, phoneme-based lip-reading, spatial-temporal convolution, transformers This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Analysis of Feature Extraction and Classification Models for Lip-Reading

Cited by 6 publications

References 18 publications

Machine Learning Approaches for Skin Neoplasm Diagnosis

Machine Learning Approaches for Skin Neoplasm Diagnosis

Machine Learning Approaches for Skin Neoplasm Diagnosis

Developing phoneme‐based lip‐reading sentences system for silent speech recognition

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