DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images

Venugopal, Vipin; Joseph, Justin; Das, Moumita; Nath, Malaya Kumar

doi:10.1016/j.compbiomed.2022.105852

Cited by 28 publications

(22 citation statements)

References 44 publications

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“…The skin cancer dataset consists of macro skin images featuring lesions with a bimodal histogram, and the PCA board image dataset consists of a background featuring different colored foreground components with unimodal or multimodal histograms. The performance of our proposed two-stage thresholding technique was evaluated quantitatively and qualitatively against other state-of-the-art thresholding techniques [ 5 , 11 , 12 , 23 , 27 , 28 , 36 , 37 , 38 , 46 ], and the results are presented in Section 6.1 and Section 6.2 .…”

Section: Resultsmentioning

confidence: 99%

“…Venugopal et al. [ 46 ] proposed DTP-Net, a deep convolutional neural network (DCNN) that aims to predict the optimal grayscale threshold value for an image. Their method is tailored to binarizing lesions on dermatological macro-images.…”

Section: Related Workmentioning

confidence: 99%

“…Depending on the characteristics of a valley, a suboptimal threshold might be computed. Moreover, histogram concavity analysis (valley and shoulder analysis) is not suitable for thresholding images with bimodal or multimodal histograms.There are advanced computer vision techniques using DCNN aimed at developing medical image segmentation approaches [ 46 , 55 ]. The DCNN-based methods do not typically work well when thresholding small objects [ 56 ] such as PCA board components.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, we propose a two-stage histogram-based automatic color thresholding technique based on the chromaticity of the image pixels. The proposed method is an unsupervised technique, as it does not require any labeled data like deep learning-based thresholding techniques [ 46 , 55 ]. The details of the proposed method are provided in the following section.…”

Section: Related Workmentioning

confidence: 99%

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A Two-Stage Automatic Color Thresholding Technique

Shamna

Ellam²,

Grübl

et al. 2023

Sensors

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Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one’s focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing background suppression in PCA boards facilitates the inspection of digital images with small objects of interest, such as text or microcontrollers on a PCA board. The segmentation of skin cancer lesions will help doctors to automate skin cancer detection. The results showed a clear and robust background–foreground separation across various sample images under different camera or lighting conditions, which the naked implementation of existing state-of-the-art thresholding methods could not achieve.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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A Two-Stage Automatic Color Thresholding Technique

Shamna

Ellam²,

Grübl

et al. 2023

Sensors

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“…Therefore, regression error is analyzed to study the models' prediction performance, and MCRMSE, which stands for Mean Column-wise Root Mean Squared Error, is proposed. The square root of the mean of the squared variations between the predictions and the ground truth is factored by the regression performance metric known as RMSE to determine the average magnitude of errors [32] [33]. The RMSE metric formula is provided in (1), in which n denotes the number of occurrences.…”

Section: Mcrmse Performance Metricmentioning

confidence: 99%

Exploration of hybrid deep learning algorithms for covid-19 mrna vaccine degradation prediction system

Ing

Abdullah²,

Mashor³

et al. 2022

Int. J. Adv. Intell. Informatics

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Coronavirus causes a global pandemic that has adversely affected public health, the economy, including every life aspect. To manage the spread, innumerable measurements are gathered. Administering vaccines is considered to be among the precautionary steps under the blueprint. Among all vaccines, the messenger ribonucleic acid (mRNA) vaccines provide notable effectiveness with minimal side effects. However, it is easily degraded and limits its application. Therefore, considering the cruciality of predicting the degradation rate of the mRNA vaccine, this prediction study is proposed. In addition, this study compared the hybridizing sequence of the hybrid model to identify its influence on prediction performance. Five models are created for exploration and prediction on the COVID-19 mRNA vaccine dataset provided by Stanford University and made accessible on the Kaggle community platform employing the two deep learning algorithms, Long Short-Term Memory (LSTM) as well as Gated Recurrent Unit (GRU). The Mean Columnwise Root Mean Square Error (MCRMSE) performance metric was utilized to assess each model’s performance. Results demonstrated that both GRU and LSTM are befitting for predicting the degradation rate of COVID-19 mRNA vaccines. Moreover, performance improvement could be achieved by performing the hybridization approach. Among Hybrid_1, Hybrid_2, and Hybrid_3, when trained with Set_1 augmented data, Hybrid_3 with the lowest training error (0.1257) and validation error (0.1324) surpassed the other two models; the same for model training with Set_2 augmented data, scoring 0.0164 and 0.0175 MCRMSE for training error and validation error, respectively. The variance in results obtained by hybrid models from experimenting claimed hybridizing sequence of algorithms in hybrid modeling should be concerned.

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