A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

Hassan, Esraa; Shams, Mahmoud Y.; Hikal, Noha A.; Elmougy, Samir

doi:10.32604/cmc.2022.025629

Cited by 12 publications

(8 citation statements)

References 31 publications

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“…To boost the results obtained, we used the confusion matrix to determine the accuracy, recall, precision, and the harmonic mean of precision and sensitivity, which is called the F1score as shown in Eqs. ( 7)-( 10) [37,38].…”

Section: Resultsmentioning

confidence: 99%

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

Tarek¹,

Shohieb²,

Elhady³

et al. 2023

Computer Systems Science and Engineering

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The ability to detect and localize the human eye is critical for use in security applications and human identification and verification systems. This is because eye recognition algorithms have multiple challenges, such as multi-pose variations, ocular parts, and illumination. Moreover, the modern security applications fail to detect facial expressions from eye images. In this paper, a Speeded-Up Roust Feature (SURF) Algorithm was utilized to localize the face images of the enrolled subjects. We highlighted on eye and pupil parts to be detected based on SURF, Hough Circle Transform (HCT), and Local Binary Pattern (LBP). Afterward, Deep Belief Neural Networks (DBNN) were used to classify the input features results from the SURF algorithm. We further determined the correctly and wrongly classified subjects using a confusion matrix with two class labels to classify people whose eye images are correctly detected. We apply Stochastic Gradient Descent (SGD) optimizer to address the overfitting problem, and the hyper-parameters are fine-tuned based on the applied DBNN. The accuracy of the proposed system is determined based on SURF, LBP, and DBNN classifier achieved 95.54% for the ORL dataset, 94.07% for the BioID, and 96.20% for the CASIA-V5 dataset. The proposed approach is more reliable and more advanced when compared with state-of-the-art algorithms.

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Section: Resultsmentioning

confidence: 99%

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

Tarek¹,

Shohieb²,

Elhady³

et al. 2023

Computer Systems Science and Engineering

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“…The input to the network was decreased thanks to the author's usage of the PCA for dimensional reduction. The research [10] indicates that the new result from the ESC ST-T database exceeds the prior one. In [11] They employed various kinds of multilayer neural networks as classi ers to identify the two types of ECG patterns.…”

Section: Ecg Classi Cation Techniquesmentioning

confidence: 85%

“…The outcomes of this research have been contrasted with other neural network architectures to determine which neural network structure is most effective for classifying types of arrhythmias. To identify ischemia arrhythmia episodes in the ECG data, a neural network was put into use [10]. The input to the network was decreased thanks to the author's usage of the PCA for dimensional reduction.…”

Section: Ecg Classi Cation Techniquesmentioning

confidence: 99%

“…To lter out the noise in the ECG signal, this paper proposes a wavelet denoising method based on an adaptive threshold, which can dynamically adjust the threshold of different decomposition scales [7][8] [9]. The basic steps are as follows: rst, the sym8 wavelet function is used to decompose the ECG signal on eight scales, then the approximation coe cients on the eighth scale and the detail coe cients on the rst scale are directly set to zero, and the self-proposed method proposed in this paper is used on the remaining scales [10] [11]. The adaptive threshold and soft threshold function process the wavelet coe cients and reconstruct the signal, and nally, obtain the demised signal.…”

Section: Introductionmentioning

confidence: 99%

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Electrocardiogram Signal Classification Based on Deep Learning Techniques

Ahmed¹

2023

Preprint

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One of the most often used diagnostic tools in medicine and healthcare is the electrocardiogram (ECG). When it comes to healthcare prediction problems requiring ECG data, deep learning techniques seem promising. This paper aims to apply deep learning techniques to classify MIT-BIH arrhythmias on publicly available datasets. A new electrocardiogram classification for employing a spectrogram of signals algorithm is proposed. The proposed model depends on convolutional neural networks to automatically learn the characteristics of features and has used convolutional neural networks to detect normal and abnormal ECG heartbeats, with an average detection accuracy of 99.22%.

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“…The results demonstrated that Faster R-CNN is the most well-trained model with an average precision of over 0.94 [29]. For automatic diagnosis of malaria, stacked CNN [30,31] and an ensemble of residual networks [32] were recently used, with accuracy rate of 99.96% and 98.08%, respectively.…”

Section: Literature Reviewmentioning

confidence: 99%

Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

Amin¹,

Hassan²,

Belhaouari³

et al. 2023

Computers, Materials &Amp; Continua

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Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are timeconsuming that require a great deal of human expertise and efforts. Computerbased automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic system, which showed promising results. However, the most common problem with these models is that they need a large amount of data for training. This paper presents a computer-aided malaria diagnosis system that combines a semi-supervised generative adversarial network and transfer learning. The proposed model is trained in a semi-supervised manner and requires less training data than conventional deep learning models. Performance of the proposed model is evaluated on a publicly available dataset of blood smear images (with malariainfected and normal class) and achieved a classification accuracy of 96.6%.

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A Novel Convolutional Neural Network Model for Malaria Cell Images Classification

Cited by 12 publications

References 31 publications

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

Eye Detection-Based Deep Belief Neural Networks and Speeded-Up Robust Feature Algorithm

Electrocardiogram Signal Classification Based on Deep Learning Techniques

Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

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