Malaria Detection Using Image Processing And Machine Learning

Maduri, Praveen Kumar; Shalu, .; Agrawal, Shobhit; Rai, Alok; Chaubey, Shubham

doi:10.1109/icac3n53548.2021.9725557

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…The most popular field in the recent past are renewable energy [22], insulators contamination forecasting [23], load forecasting [24], Electrical Engineering [25], and energy engineering [26]. Using image processing, Kirti Motwani [27] detected malaria over blood smears images. Preprocessing is an essential part of feature extraction from images.…”

Section: Related Researchmentioning

confidence: 99%

BwMMV-pred: A Novel Ensemble learning approach using blood smear images for Malaria Prediction

Tayyeb,

Alim,

Alam

et al. 2023

Preprint

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The utilization of machine learning in healthcare is widespread, aiding doctors and clinicians in various capacities. One of the critical implementations of machine learning in healthcare is the creation of an automated diagnosis system capable of predicting outcomes equivalent to or surpassing those of human experts. This study illustrated the optimistic outcomes using the proposed Blending with Meta Majority Voting (BwMMV) method as a cutting-edge and highly effective ensemble learning technique. Essentially, the Local Binary Pattern Histogram (LBPH), a straightforward and effective texture operator, was utilized to convert the blood smear images into their underlying texture characteristics. The proposed approach BwMMV involves creating a new dataset by using the predictions generated by the 8 base classifiers, also known as an intermediate dataset. This intermediate dataset is then used to develop five additional models using 5 distinct machine-learning algorithms, called meta-models. The final decision is made using a majority voting technique, where the outcome with the highest number of votes is selected as the final choice. The proposed technique has been found to be highly effective and resilient in terms of both performance and robustness. It has been demonstrated to be a superior approach for an automatic diagnosis system when compared to conventional hard voting and blending techniques.

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Section: Related Researchmentioning

confidence: 99%

BwMMV-pred: A Novel Ensemble learning approach using blood smear images for Malaria Prediction

Tayyeb,

Alim,

Alam

et al. 2023

Preprint

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“…With a training ACC of 97.55%, the ResNet 50 model performed quite well. Maduri et al [30] implemented the CNN network and image datasets in this work. This model's primary focus is image processing with the Keras image generator to produce the results.…”

Section: Related Workmentioning

confidence: 99%

Deep learning-based computer assisted detection techniques for malaria parasite using blood smear images

2023

IJATEE

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Malaria remains a significant global health concern, impacting various regions worldwide. Achieving effective treatment and reducing mortality rates hinges on early and accurate diagnosis. In the year 2021, the World Health Organization (WHO) reported a staggering 619,000 deaths attributed to malaria. Additionally, approximately 214 million individuals were afflicted by this disease during that period. Hence, this study introduces two distinct deep-learning algorithms tailored for malaria disease classification. The first method employs a binary classifier convolutional neural network (CNN) model, attaining an accuracy (ACC) of 90.20%. The second method introduces a customized CNN model that exhibits even greater ACC, reaching an impressive 96.02%. These advanced deep learning (DL) techniques hold the potential to enhance the precision (PRE) and efficiency of malaria diagnosis, ultimately facilitating early disease detection. The study provides comprehensive insights into the proposed models. Model 1 involves malaria disease classification employing a CNN-based binary classifier, while Model 2 adopts a customized CNN architecture. The methodology section elucidates the details of these models, their design, and the execution of experiments undertaken to evaluate their performance. Notably, the proposed method is juxtaposed with the state-of-the-art approach, demonstrating superior results in accurately discerning infected and uninfected malaria blood cell images.

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“…As the development of computer vision progresses by implementing machine learning or deep learning methods, they can be used to classify medical images quickly and accurately [6]. Canonically, image classification with machine learning approaches such as Naive Bayes, Decision Tree, Linear Discriminant, Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN) has been widely used in general for malaria classification [7][8] [9][10] [11]. However, the challenge of using machine learning is the suitability of selecting the type of feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation

Muhandisin¹,

Azhar²

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Based on the WHO Report related to malaria, it is estimated that there will be 241 million malaria cases and 627,000 deaths from this disease globally in 2020 with the number of deaths increasing yearly. Preventing malaria disease conditions is through early detection. A more quick and precise malaria diagnosis method was required to simplify and reduce the detection process. Medical image classification could be carried out rapidly and precisely using machine learning or deep learning techniques. This research aims to diagnose malaria by classifying images of malaria blood cells using Deep Learning with a Transfer Learning approach. By utilizing various fine-tuning procedures and implementing data augmentation proposed method develops the method from previous studies. Two types of models Frozen ResNet50 and Fine-Tune ResNet50 are being tested. The dataset utilized will be augmented to improve model performance. This study makes use of the "NIH Malaria Cell Images Dataset" a dataset that contains a total of 27,660 image data. It is divided into two classes: parasitized and uninfected. The results are improved from previous research using the fine-tuned VGG16 model with an accuracy of 96% compared to this study using the fine-tuned ResNet50 model which achieved an accuracy score of 98%.

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Malaria Detection Using Image Processing And Machine Learning

Cited by 7 publications

References 5 publications

BwMMV-pred: A Novel Ensemble learning approach using blood smear images for Malaria Prediction

BwMMV-pred: A Novel Ensemble learning approach using blood smear images for Malaria Prediction

Deep learning-based computer assisted detection techniques for malaria parasite using blood smear images

Malaria Blood Cell Image Classification using Transfer Learning with Fine-Tune ResNet50 and Data Augmentation

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