Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Alharithi, Fahd S.; Almulihi, Ahmed; Bourouis, Sami; Alroobaea, Roobaea; Bouguila, Nizar

doi:10.3390/s21072450

Cited by 17 publications

(11 citation statements)

References 61 publications

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“…The deep learning revolution has played a great role, with the suggestion of better architectures of the convolutional neural network [ 26 , 27 , 28 , 29 , 30 , 31 ]. In the following paper, a model is presented to classify skin cancer with the help of dermoscopy images.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

et al. 2022

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Skin cancer is the most commonly diagnosed and reported malignancy worldwide. To reduce the death rate from cancer, it is essential to diagnose skin cancer at a benign stage as soon as possible. To save lives, an automated system that can detect skin cancer in its earliest stages is necessary. For the diagnosis of skin cancer, various researchers have performed tasks using deep learning and transfer learning models. However, the existing literature is limited in terms of its accuracy and its troublesome and time-consuming process. As a result, it is critical to design an automatic system that can deliver a fast judgment and considerably reduce mistakes in diagnosis. In this work, a deep learning-based model has been designed for the identification of skin cancer at benign and malignant stages using the concept of transfer learning approach. For this, a pre-trained VGG16 model is improved by adding one flatten layer, two dense layers with activation function (LeakyReLU) and another dense layer with activation function (sigmoid) to enhance the accuracy of this model. This proposed model is evaluated on a dataset obtained from Kaggle. The techniques of data augmentation are applied in order to enhance the random-ness among the input dataset for model stability. The proposed model has been validated by considering several useful hyper parameters such as different batch sizes of 8, 16, 32, 64, and 128; different epochs and optimizers. The proposed model is working best with an overall accuracy of 89.09% on 128 batch size with the Adam optimizer and 10 epochs and outperforms state-of-the-art techniques. This model will help dermatologists in the early diagnosis of skin cancers.

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Section: Introductionmentioning

confidence: 99%

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

et al. 2022

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“…Spatial classification is an important research area for facial expression classification [39][40][41][42][43][44][45] and image recognition [45][46][47][48][49][50]. In this research, we are concentrating on human facial expression classification (HFE) through a set of video frames.…”

Section: Facial Expression Classificationmentioning

confidence: 99%

Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

Mahmoud¹,

Hafez²,

Althukair³

2023

Intelligent Automation &Amp; Soft Computing

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Infinite Gaussian mixture process is a model that computes the Gaussian mixture parameters with order. This process is a probability density distribution with adequate training data that can converge to the input density curve. In this paper, we propose a data mining model namely Beta hierarchical distribution that can solve axial data modeling. A novel hierarchical Two-Hyper-Parameter Poisson stochastic process is developed to solve grouped data modelling. The solution uses data mining techniques to link datum in groups by linking their components. The learning techniques are novel presentations of Gaussian modelling that use prior knowledge of the representation hyper-parameters and approximate them in a closed form. Experiments are performed on axial data modeling of Arabic Script classification and depict the effectiveness of the proposed method using a hand written benchmark dataset which contains complex handwritten Arabic patterns. Experiments are also performed on the application of facial expression recognition and prove the accuracy of the proposed method using a benchmark dataset which contains eight different facial expressions.

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“…Visual multimedia recognition has been a challenging research topic which could attract many applications such as actions recognition [40,41], image categorization [42,43], biomedical image recognition [44], and facial expressions [29,30]. In this work, we are focusing on a particular problem that has received a lot of attention namely Human actions recognition (HAR) through sequence of videos.…”

Section: Human Actions Categorizationmentioning

confidence: 99%

Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

Alghamdi¹,

Bourouis²,

Bouguila³

2022

Computers, Materials &Amp; Continua

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Interest in automated data classification and identification systems has increased over the past years in conjunction with the high demand for artificial intelligence and security applications. In particular, recognizing human activities with accurate results have become a topic of high interest. Although the current tools have reached remarkable successes, it is still a challenging problem due to various uncontrolled environments and conditions. In this paper two statistical frameworks based on nonparametric hierarchical Bayesian models and Gamma distribution are proposed to solve some realworld applications. In particular, two nonparametric hierarchical Bayesian models based on Dirichlet process and Pitman-Yor process are developed. These models are then applied to address the problem of modelling grouped data where observations are organized into groups and these groups are statistically linked by sharing mixture components. The choice of the Gamma mixtures is motivated by its flexibility for modelling heavy-tailed distributions. In addition, deploying the Dirichlet process prior is justified by its advantage of automatically finding the right number of components and providing nice properties. Moreover, a learning step via variational Bayesian setting is presented in a flexible way. The priors over the parameters are selected appropriately and the posteriors are approximated effectively in a closed form. Experimental results based on a real-life applications that concerns texture classification and human actions recognition show the capabilities and effectiveness of the proposed framework.

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Discriminative Learning Approach Based on Flexible Mixture Model for Medical Data Categorization and Recognition

Cited by 17 publications

References 61 publications

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

An Enhanced Transfer Learning Based Classification for Diagnosis of Skin Cancer

Data Mining Approach Based on Hierarchical Gaussian Mixture Representation Model

Effective Frameworks Based on Infinite Mixture Model for Real-World Applications

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