Automated Detection of Malaria implemented by Deep Learning in Pytorch

Krishnadas, Padmini; Sampathila, Niranjana

doi:10.1109/conecct52877.2021.9622608

Cited by 16 publications

(9 citation statements)

References 10 publications

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“…The model could be made more robust if it were trained with more data. The intelligent algorithms are promising with the optimized features for screening various problems in digital pathology [ 38 , 39 , 40 ]. The improved telehealth framework with the intelligent algorithm will enable the remote diagnosis facility [ 41 , 42 , 43 ].…”

Section: Resultsmentioning

confidence: 99%

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

et al. 2022

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Acute lymphoblastic leukemia (ALL) is a rare type of blood cancer caused due to the overproduction of lymphocytes by the bone marrow in the human body. It is one of the common types of cancer in children, which has a fair chance of being cured. However, this may even occur in adults, and the chances of a cure are slim if diagnosed at a later stage. To aid in the early detection of this deadly disease, an intelligent method to screen the white blood cells is proposed in this study. The proposed intelligent deep learning algorithm uses the microscopic images of blood smears as the input data. This algorithm is implemented with a convolutional neural network (CNN) to predict the leukemic cells from the healthy blood cells. The custom ALLNET model was trained and tested using the microscopic images available as open-source data. The model training was carried out on Google Collaboratory using the Nvidia Tesla P-100 GPU method. Maximum accuracy of 95.54%, specificity of 95.81%, sensitivity of 95.91%, F1-score of 95.43%, and precision of 96% were obtained by this accurate classifier. The proposed technique may be used during the pre-screening to detect the leukemia cells during complete blood count (CBC) and peripheral blood tests.

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Section: Resultsmentioning

confidence: 99%

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

et al. 2022

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“…The paper reported a training accuracy of 95.91%. Krishnadas et al (2021) reported an algorithm for the detection of malaria implemented by deep learning using PyTorch. Pretrained models of DenseNet121 and Resnet50 are repurposed with transfer learning to fit the task of malaria detection in segmented single-cell images [22].…”

Section: Literature Reviewmentioning

confidence: 99%

Classification of Malaria Using Object Detection Models

et al. 2022

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Malaria poses a global health problem every day, as it affects millions of lives all over the world. A traditional diagnosis requires the manual inspection of blood smears from the patient under a microscope to check for the malaria parasite. This is often time consuming and subject to error. Thus, the automated detection and classification of the malaria type and stage of progression can provide a quicker and more accurate diagnosis for patients. In this research, we used two object detection models, YOLOv5 and scaled YOLOv4, to classify the stage of progression and type of malaria parasite. We also used two different datasets for the classification of stage and parasite type while assessing the viability of the dataset for the task. The dataset used is comprised of microscopic images of red blood cells that were either parasitized or uninfected. The infected cells were classified based on two broad categories: the type of malarial parasite causing the infection and the stage of progression of the disease. The dataset was manually annotated using the LabelImg tool. The images were then augmented to enhance model training. Both models YOLOv5 and scaled YOLOv4 proved effective in classifying the type of parasite. Scaled YOLOv4 was in the lead with an accuracy of 83% followed by YOLOv5 with an accuracy of 78.5%. The proposed models may be useful for the medical professionals in the accurate diagnosis of malaria and its stage prediction.

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“…Regarding malaria parasite detection using machine learning techniques, much work has been performed. For example, in [ 7 ], the authors used powerful classifiers (namely ResNet and DenseNet) via the transfer learning technique to classify cell images as either parasitized or uninfected. Another approach to tackling the problem of detecting malaria in blood images can be found in [ 8 ], where the work was based on different machine learning methods (decision tree, support vector machine, naïve Bayes, and K-nearest neighbor), which were fit on six features extracted by the VGG16, VGG19, ResNet50, ResNet101, DenseNet121, and DenseNet201 models.…”

Section: Introductionmentioning

confidence: 99%

“…Such a manual approach to machine learning for cell image classification of malaria requires experts in machine learning to prepare the model. However, the works [ 7 , 8 ], as well as the greatly different works on machine learning (ML) adopted for the malaria detection problem [ 9 ] suffer from different issues: the first one is that human effort is required in order to extract the most-valuable features to feed the classifiers efficiently. Furthermore, this approach requires specialists in ML and DL to build a robust model, in addition to choosing the best hyperparameters of the model to distinguish the malaria images from the normal ones.…”

Section: Introductionmentioning

confidence: 99%

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

et al. 2023

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Automated deep learning is promising in artificial intelligence (AI). However, a few applications of automated deep learning networks have been made in the clinical medical fields. Therefore, we studied the application of an open-source automated deep learning framework, Autokeras, for detecting smear blood images infected with malaria parasites. Autokeras is able to identify the optimal neural network to perform the classification task. Hence, the robustness of the adopted model is due to it not needing any prior knowledge from deep learning. In contrast, the traditional deep neural network methods still require more construction to identify the best convolutional neural network (CNN). The dataset used in this study consisted of 27,558 blood smear images. A comparative process proved the superiority of our proposed approach over other traditional neural networks. The evaluation results of our proposed model achieved high efficiency with impressive accuracy, reaching 95.6% when compared with previous competitive models.

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Automated Detection of Malaria implemented by Deep Learning in Pytorch

Cited by 16 publications

References 10 publications

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Customized Deep Learning Classifier for Detection of Acute Lymphoblastic Leukemia Using Blood Smear Images

Classification of Malaria Using Object Detection Models

Autokeras Approach: A Robust Automated Deep Learning Network for Diagnosis Disease Cases in Medical Images

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