Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID‐19 classification

Akbar, Sumaiya Begum; Thanupillai, Kalaiselvi; Sundararaj, Suganthi

doi:10.1002/cpe.6958

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…The Digital Database of Thyroid Images (DDTI) dataset, provided by Columbia National University, is used in this study to classify thyroid nodules [20]. This collection includes 480 US pictures from 400 thyroid illness patients.…”

Section: Dataset Collectionmentioning

confidence: 99%

Classification of Thyroid Nodules Using Follow the Regularized Leader Optimization Based Deep Neural Networks

Beyyala,

Priya,

Choudari

et al. 2023

RIA

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Thyroid cancer, the most common endocrine malignancy, has exhibited a steadily increasing incidence worldwide. Recently, ultrasound (US) imaging has been recognized as a potential diagnostic tool for early assessment of thyroid nodules. However, visual interpretation of nodules is subject to the radiologists' subjective evaluations. To address this, a computer-aided Diagnostic (CAD) system has been developed to differentiate between benign and malignant thyroid nodules. The efficiency of this nodule classifier is heavily dependent on the features employed in the classification process. In this study, the efficacy of the RREMI-RF approach, employing the Multi-Channel Convolutional Neural Network (MCNN) and Hybrid Feature Cropping Network (HFCN) techniques, was evaluated. An innovative Follow the Regularised Leader-based Deep Neural Network (FTRL-DNN) technique is proposed for the precise classification of thyroid nodules. In this method, the AlexNet learning-based feature extraction system was utilized to extract features during the classification process. Images from the Digital Database Thyroid Image (DDTI) dataset were classified using the Long Short Term Memory (LSTM) classifier. Performance metrics, including accuracy, sensitivity, specificity, precision, and error rate, were used to assess the effectiveness of the FTRL-DNN algorithm. Compared to the HFCN and MCNN, the FTRL-DNN-based thyroid nodule classification demonstrated superior accuracy, achieving a rate of 98.94%. This research presents a significant advancement in the automated classification of thyroid nodules, potentially improving early detection and treatment of thyroid cancer.

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Section: Dataset Collectionmentioning

confidence: 99%

Classification of Thyroid Nodules Using Follow the Regularized Leader Optimization Based Deep Neural Networks

Beyyala,

Priya,

Choudari

et al. 2023

RIA

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“…They analyzed word embedding and neural organization-based ways to deal with SC. A DNN based on a mix of CNN and BiLSTM was proposed and characterized for detection and classification framework based on anopheles search-optimized AlexNet convolution [22][23]. To tackle the Web service categorization challenge, Kang et al [24] developed a topical attention-based BiLSTM model.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding

Pimpalkar

Raj

2023

Int. j. eng. technol. innov.

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Sentiment analysis (SA) has become an essential component of natural language processing (NLP) with numerous practical applications to understanding “what other people think”. Various techniques have been developed to tackle SA using deep learning (DL); however, current research lacks comprehensive strategies incorporating multiple-word embeddings. This study proposes a self-attention mechanism that leverages DL and involves the contextual integration of word embedding with a time-dispersed bidirectional gated recurrent unit (Bi-GRU). This work employs word embedding approaches GloVe, word2vec, and fastText to achieve better predictive capabilities. By integrating these techniques, the study aims to improve the classifier’s capability to precisely analyze and categorize sentiments in textual data from the domain of movies. The investigation seeks to enhance the classifier’s performance in NLP tasks by addressing the challenges of underfitting and overfitting in DL. To evaluate the model’s effectiveness, an openly available IMDb dataset was utilized, achieving a remarkable testing accuracy of 99.70%.

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Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID‐19 classification

Akbar¹,

Thanupillai²,

Sundararaj

2022

Concurrency and Computation

Self Cite

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In this article, COVID‐19 detection and classification framework based on anopheles search optimized AlexNet convolutional deep neural network for random forest classifier is implemented. Here, the COVID‐19 dataset is taken from Joseph Paul Cohen database. Then, the input images are preprocessed with the help of fuzzy gray level difference histogram equalization technique (FGLHE) and fuzzy stacking technique for color enhancement and noise elimination in the input images. The FGLHE technique and fuzzy stacking technique are combined together and forms into stacked dataset image. This stacked dataset are trained with AlexNet convolutional deep neural network model and the feature packages acquired via the models are processed by the anopheles search algorithm. Subsequently, the efficient features are combined and delivered to random forest (RF) classifier. The proposed approach is implemented in MATLAB. The proposed ADCNN‐ASA‐RFC provides 91.66%, 69.13%, 34.86%, and 70.13% higher accuracy, 79.13%, 60.33%, and 63.34% higher specificity and 77.13%, 58.45%, 25.86%, and 55.33%, higher sensitivity compared with existing algorithms. At last, the simulation outcomes demonstrate that the proposed system can be able to find the optimal solutions efficiently and accurately with COVID‐19 diagnosis.

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Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID‐19 classification

Cited by 8 publications

References 35 publications

Classification of Thyroid Nodules Using Follow the Regularized Leader Optimization Based Deep Neural Networks

Classification of Thyroid Nodules Using Follow the Regularized Leader Optimization Based Deep Neural Networks

A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding

Combining the advantages of AlexNet convolutional deep neural network optimized with anopheles search algorithm based feature extraction and random forest classifier for COVID‐19 classification

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