Computer aided tool for automatic detection and delineation of nucleus from oral histopathology images for OSCC screening

Das, Dev Kumar; Koley, Subhranil; Bose, S. K.; Maiti, Ananda; Mitra, Bhaskar; Mukherjee, Gopeswar; Dutta, Pranab

doi:10.1016/j.asoc.2019.105642

Cited by 18 publications

(20 citation statements)

References 27 publications

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“…Nowadays, researchers are using CNN (Convolutional Neural Network), a class of deep learning neural networks for several applications [31] , [32] , [33] . CNNs have an input layer, an output layer, and hidden layers [34] .…”

Section: Introductionmentioning

confidence: 99%

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques

Agarwal

Díez

2020

Applied Soft Computing

314

151

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COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors’ knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.

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Section: Introductionmentioning

confidence: 99%

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Marques

Agarwal

Díez

2020

Applied Soft Computing

314

151

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“…The majority of these studies used a convolutional neural network (CNN) [ 2 , 15 – 22 , 24 – 26 , 28 , 31 – 36 , 38 – 41 , 43 – 45 , 48 , 49 ]. Several data types such as gene expression data [ 15 , 45 ], spectra data [ 20 , 21 , 29 , 34 , 37 , 44 , 48 ], and other image data types—anatomical [ 16 ], intraoral [ 17 ], histology [ 18 , 27 ], auto-fluorescence [ 19 , 22 ], cytology-image [ 23 ], neoplastic [ 40 ], clinical [ 28 , 36 , 38 ], oral lesions [ 42 ], computed tomography images [ 24 – 26 , 33 , 35 , 41 , 49 ], clinicopathologic [ 2 ], saliva metabolites [ 31 ], histopathological [ 30 , 32 , 43 ], and pathological [ 39 ] images have been used in the included studies.…”

Section: Resultsmentioning

confidence: 99%

“…Similarly, a total of 11 studies reported the combination of the trio of sensitivity, specificity, and accuracy as the performance metrics for the deep machine learning method [ 15 , 19 – 21 , 24 , 25 , 30 , 35 , 37 , 38 , 42 ]. Both specificity and sensitivity were used to depict the performance of the model [ 17 , 20 , 22 , 27 , 48 ]. Additionally, specificity and accuracy were also used to demonstrate the performance of the deep learning model for prognostication in OSCC [ 23 ].…”

Section: Resultsmentioning

confidence: 99%

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Alabi

Bello

Youssef

et al. 2021

Front. Oral. Health

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The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases—PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.

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“…Nuclei localization and segmentation based on CNN has been given in many previous researches [16][17][18][19][20]. In a work [21], CNN was used to identify miotic and nonmiotic cells focusing on information gathered from color, shape, and texture.…”

Section: Literature Reviewmentioning

confidence: 99%

Cancer Cell Detection through Histological Nuclei Images Applying the Hybrid Combination of Artificial Bee Colony and Particle Swarm Optimization Algorithms

Alsarori¹,

Kaya²,

Rahebi³

et al. 2020

IJCIS

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Cancer is a fatal disease that is continuously growing in the developed countries. It is also considered as a main global human health problem. Based on several studies, which have been conducted so far, we found out that Hybrid Particle Swarm Optimization and Artificial Bee Colony Algorithm has never been used in any relevant study; so, in this study we purposed using this algorithm for detecting the centers of the nuclei with the help of histological images to obtain accurate results. If we compare this algorithm with previously proposed algorithms, this algorithm doesn't require a lot of parameters, and besides, it is faster, simpler, and more flexible. This study has been carried out on histological images obtained from a database containing 810 microscopic slides of stained H&E samples from PSB-2015 crowd-sourced nuclei dataset. During the determination process, the noise on images was first eliminated using morphological techniques, and then, we used Hybrid PSO-ABC algorithm to for segmentation of the nucleic images and compared the results with other optimization algorithms to test its accuracy and efficiency. The average 99.38% accuracy rate was assured for cancer nuclei. To demonstrate the robustness of this experiment, the results were compared with other known cancer nuclei detection procedures, which are already mentioned in the literature. Using the new proposed algorithm showed the highest accuracy when it was compared to rest of the methods. The high value outcome confirms that the suggested method outperformed as compared to other algorithms because it shows a higher distinctive ability.

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Computer aided tool for automatic detection and delineation of nucleus from oral histopathology images for OSCC screening

Cited by 18 publications

References 27 publications

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network

Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

Cancer Cell Detection through Histological Nuclei Images Applying the Hybrid Combination of Artificial Bee Colony and Particle Swarm Optimization Algorithms

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