A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

Umer, Muhammad; Sadiq, Saima; Ahmad, Muhammad; Ullah, Saleem; Choi, Gyu Sang; Mehmood, Arif

doi:10.1109/access.2020.2994810

Cited by 84 publications

(37 citation statements)

References 45 publications

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“…Due to the very promising results provided by CNNs in medical image analysis and classification, they are considered as de facto standard in this domain [9,10]. CNN has been used for a variety of classification tasks related to medical diagnosis such as lung disease [10], detection of malarial parasite in images of thin blood smear [11], breast cancer detection [12], wireless endoscopy images [13], interstitial lung disease [14], CAD-based diagnosis in chest radiography [15], diagnosis of skin cancer by classification [16], and automatic diagnosis of various chest diseases using chest X-ray image classification [17]. Since the emergence of COVID-19 in December 2019,…”

Section: Related Workmentioning

confidence: 99%

An Efficient CNN Model for COVID‐19 Disease Detection Based on X‐Ray Image Classification

et al. 2021

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Artificial intelligence (AI) techniques in general and convolutional neural networks (CNNs) in particular have attained successful results in medical image analysis and classification. A deep CNN architecture has been proposed in this paper for the diagnosis of COVID-19 based on the chest X-ray image classification. Due to the nonavailability of sufficient-size and good-quality chest X-ray image dataset, an effective and accurate CNN classification was a challenge. To deal with these complexities such as the availability of a very-small-sized and imbalanced dataset with image-quality issues, the dataset has been preprocessed in different phases using different techniques to achieve an effective training dataset for the proposed CNN model to attain its best performance. The preprocessing stages of the datasets performed in this study include dataset balancing, medical experts’ image analysis, and data augmentation. The experimental results have shown the overall accuracy as high as 99.5% which demonstrates the good capability of the proposed CNN model in the current application domain. The CNN model has been tested in two scenarios. In the first scenario, the model has been tested using the 100 X-ray images of the original processed dataset which achieved an accuracy of 100%. In the second scenario, the model has been tested using an independent dataset of COVID-19 X-ray images. The performance in this test scenario was as high as 99.5%. To further prove that the proposed model outperforms other models, a comparative analysis has been done with some of the machine learning algorithms. The proposed model has outperformed all the models generally and specifically when the model testing was done using an independent testing set.

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Section: Related Workmentioning

confidence: 99%

An Efficient CNN Model for COVID‐19 Disease Detection Based on X‐Ray Image Classification

et al. 2021

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“…It provides promising results with end to end modeling with out manual feature engineering in medical image classification Umer et al. ( 2020 ), multi-label image classification He et al. ( 2020 ), text categorization Imtiaz et al.…”

Section: Related Workmentioning

confidence: 99%

COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images

Umer

Ashraf

Ullah

et al. 2021

J Ambient Intell Human Comput

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COVID-19 pandemic is widely spreading over the entire world and has established significant community spread. Fostering a prediction system can help prepare the officials to respond properly and quickly. Medical imaging like X-ray and computed tomography (CT) can play an important role in the early prediction of COVID-19 patients that will help the timely treatment of the patients. The x-ray images from COVID-19 patients reveal the pneumonia infections that can be used to identify the patients of COVID-19. This study presents the use of Convolutional Neural Network (CNN) that extracts the features from chest x-ray images for the prediction. Three filters are applied to get the edges from the images that help to get the desired segmented target with the infected area of the x-ray. To cope with the smaller size of the training dataset, Keras' ImageDa-taGenerator class is used to generate ten thousand augmented images. Classification is performed with two, three, and four classes where the four-class problem has X-ray images from COVID-19, normal people, virus pneumonia, and bacterial pneumonia. Results demonstrate that the proposed CNN model can predict COVID-19 patients with high accuracy. It can help automate screening of the patients for COVID-19 with minimal contact, especially areas where the influx of patients can not be treated by the available medical staff. The performance comparison of the proposed approach with VGG16 and AlexNet shows that classification results for two and four classes are competitive and identical for three-class classification.

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“…Deep learning has been utilized by many researchers for image classification [10] and tweet classification [11]. Rustam et al [12] presented a Tweets Classification for US Airline Companies Sentiments.…”

Section: Related Workmentioning

confidence: 99%

“…ML models are evaluated on many commonly used performance indicators such as accuracy, recall, precision whereas TP is true positive, FP is false positive, TN is true negative, and FN is false negative and can be defined as [10]. TP: TP represents the positive predictions of a correctly predicted class.…”

Section: E Evaluation Metricsmentioning

confidence: 99%

Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)

et al. 2021

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The proliferation of user-generated content on social media has made opinion mining an arduous job. As a microblogging platform, Twitter is being used to collect views about products, trends, and politics. Sentiment analysis is a technique used to analyze the attitude, emotions and opinions of different people towards anything, and it can be carried out on tweets to analyze public opinion on news, policies, social movements, and personalities. By employing Machine Learning models, opinion mining can be performed without reading tweets manually. Their results could assist governments and businesses in rolling out policies, products, and events. Seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. With an in-depth comparative performance analysis, it was observed that proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score. To further validate stability of the proposed approach on two more datasets, one binary and other multi-class dataset and achieved robust results.

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A Novel Stacked CNN for Malarial Parasite Detection in Thin Blood Smear Images

Cited by 84 publications

References 45 publications

An Efficient CNN Model for COVID‐19 Disease Detection Based on X‐Ray Image Classification

An Efficient CNN Model for COVID‐19 Disease Detection Based on X‐Ray Image Classification

COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images

Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)

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