Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method

Chatterjee, Somnath; Biswas, Shreya; Majee, Arindam; Sen, Shibaprasad; Oliva, Diego; Sarkar, Ram

doi:10.1016/j.compbiomed.2021.105027

Cited by 38 publications

(22 citation statements)

References 63 publications

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“…The dimensionality of the feature space may be reduced to improve the classification performances of a dataset, including both dimension reduction [ 26 ] and feature selection [ 27 ] algorithms. It is anticipatable that the removal of irrelevant features will improve the efficiencies of both training and predicting tasks.…”

Section: Methodsmentioning

confidence: 99%

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

Zhang

Zhu

et al. 2022

Briefings in Bioinformatics

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Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of most human cells and exerts an essential role in the immune system responding to the invasive items. The T cell antigen receptors may recognize the HLA-peptide complexes on the surfaces of cancer cells and destroy these cancer cells through toxic T lymphocytes. The computational determination of HLA-binding peptides will facilitate the rapid development of cancer immunotherapies. This study hypothesized that the natural language processing-encoded peptide features may be further enriched by another deep neural network. The hypothesis was tested with the Bi-directional Long Short-Term Memory-extracted features from the pretrained Protein Bidirectional Encoder Representations from Transformers-encoded features of the class I HLA (HLA-I)-binding peptides. The experimental data showed that our proposed HLAB feature engineering algorithm outperformed the existing ones in detecting the HLA-I-binding peptides. The extensive evaluation data show that the proposed HLAB algorithm outperforms all the seven existing studies on predicting the peptides binding to the HLA-A*01:01 allele in AUC and achieves the best average AUC values on the six out of the seven k-mers (k=8,9,...,14, respectively represent the prediction task of a polypeptide consisting of k amino acids) except for the 9-mer prediction tasks. The source code and the fine-tuned feature extraction models are available at http://www.healthinformaticslab.org/supp/resources.php.

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Section: Methodsmentioning

confidence: 99%

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

Zhang

Zhu

et al. 2022

Briefings in Bioinformatics

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“…Thermography infrared is another technique for medical imaging which can be used in the breast cancer diagnosis process. This technique is used in collecting DMR-IR dataset which is adopted widely to evaluate state-of-the-art breast cancer diagnosis methods such as the proposed methods by Gonçalves et al (2022) [19] and Chatterjee et al (2022) [20]. Similar to other mentioned works, Gonçalves et al also used pre-trained CNNs.…”

Section: Related Workmentioning

confidence: 99%

“…In that paper, two bio-inspired algorithms (i.e., Genetic Algorithm and Particle swarm optimization) were suggested as searching methods. In the study by Chatterjee et al [20] pre-trained version of VGG-16 was used to extract image features as well. The distinctive point of their study is to use a memory-optimized version of the Dragonfly Algorithm to reduce the dimension of the features vector by about 40% before adopting an SVM classifier.…”

Section: Related Workmentioning

confidence: 99%

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Abbasniya

Sheikholeslamzadeh²,

Nasiri³

et al. 2022

Computers and Electrical Engineering

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“…Therefore, the use of some FS methods might help in improving the final output. Based on this, Chatterjee et al [37] proposed a deep feature selection technique. In this method, the authors improved the Dragonfly Algorithm (DA) with the help of the Grunwald-Letnikov method to perform FS.…”

Section: Fig 8 Examples Of Different Forms Of Microscopic Biopsy Imag...mentioning

confidence: 99%

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

Malakar¹,

Roy

Das

et al. 2022

Arch Computat Methods Eng

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Disease prediction from diagnostic reports and pathological images using artificial intelligence (AI) and machine learning (ML) is one of the fastest emerging applications in recent days. Researchers are striving to achieve near-perfect results using advanced hardware technologies in amalgamation with AI and ML based approaches. As a result, a large number of AI and ML based methods are found in the literature. A systematic survey describing the state-of-the-art disease prediction methods, specifically chronic disease prediction algorithms, will provide a clear idea about the recent models developed in this field. This will also help the researchers to identify the research gaps present there. To this end, this paper looks over the approaches in the literature designed for predicting chronic diseases like Breast Cancer, Lung Cancer, Leukemia, Heart Disease, Diabetes, Chronic Kidney Disease and Liver Disease. The advantages and disadvantages of various techniques are thoroughly explained. This paper also presents a detailed performance comparison of different methods. Finally, it concludes the survey by highlighting some future research directions in this field that can be addressed through the forthcoming research attempts.

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Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method

Cited by 38 publications

References 63 publications

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction

Classification of Breast Tumors Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods

Computer Based Diagnosis of Some Chronic Diseases: A Medical Journey of the Last Two Decades

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