Deep Learning‐Based Image Classifier for Malaria Cell Detection

Negi, Atul; Kumar, Krishan; Chauhan, Prachi

doi:10.1002/9781119792611.ch12

Cited by 29 publications

(11 citation statements)

References 13 publications

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“…Deep learning algorithms trained from scratch face current problems such as overfitting, underfitting, and gradient disappearance. Here, (i) data augmentation method can be applied to solve the problems experienced [ 3 ], (ii) deep neural networks can be developed with residual networks or additional feature vectors (iii) transfer learning technique can be applied [ 4 ]. Transfer learning practice between solutions draws attention as an essential solution technique.…”

Section: Discussionmentioning

confidence: 99%

MediNet: transfer learning approach with MediNet medical visual database

Reis

Turk

Khoshelham

et al. 2023

Multimed Tools Appl

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The rapid development of machine learning has increased interest in the use of deep learning methods in medical research. Deep learning in the medical field is used in disease detection and classification problems in the clinical decision-making process. Large amounts of labeled datasets are often required to train deep neural networks; however, in the medical field, the lack of a sufficient number of images in datasets and the difficulties encountered during data collection are among the main problems. In this study, we propose MediNet, a new 10-class visual dataset consisting of Rontgen (X-ray), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, and Histopathological images such as calcaneal normal, calcaneal tumor, colon benign colon adenocarcinoma, brain normal, brain tumor, breast benign, breast malignant, chest normal, chest pneumonia. AlexNet, VGG19-BN, Inception V3, DenseNet 121, ResNet 101, EfficientNet B0, Nested-LSTM + CNN, and proposed RdiNet deep learning algorithms are used in the transfer learning for pre-training and classification application. Transfer learning aims to apply previously learned knowledge in a new task. Seven algorithms were trained with the MediNet dataset, and the models obtained from these algorithms, namely feature vectors, were recorded. Pre-training models were used for classification studies on chest X-ray images, diabetic retinopathy, and Covid-19 datasets with the transfer learning technique. In performance measurement, an accuracy of 94.84% was obtained in the traditional classification study for the InceptionV3 model in the classification study performed on the Chest X-Ray Images dataset, and the accuracy was increased 98.71% after the transfer learning technique was applied. In the Covid-19 dataset, the classification success of the DenseNet121 model before pre-trained was 88%, while the performance after the transfer application with MediNet was 92%. In the Diabetic retinopathy dataset, the classification success of the Nested-LSTM + CNN model before pre-trained was 79.35%, while the classification success was 81.52% after the transfer application with MediNet. The comparison of results obtained from experimental studies observed that the proposed method produced more successful results. Graphical abstract

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Section: Discussionmentioning

confidence: 99%

MediNet: transfer learning approach with MediNet medical visual database

Reis

Turk

Khoshelham

et al. 2023

Multimed Tools Appl

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“…Precision is described as number of positive instances divided by number of TPs and number of FPs, and this is given as (4).…”

Section: Performance Metricsmentioning

confidence: 99%

“…Significant applications [1]- [3] and methods are currently increasing the demand for artificial intelligence (AI) approaches. The use of AI approaches in the health field has shown promising results, such as the identification of malaria cells [4]. The study of plant diseases and their severity 1031 estimate has gained a lot of interest from academics researchers and in recent years as a result of technological advancement [5].…”

Section: Introductionmentioning

confidence: 99%

Identifying corn leaves diseases by extensive use of transfer learning: a comparative study

Hatem

Altememe

Fadhel

2023

IJEECS

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<span lang="EN-US">Deep learning is currently playing an important role in image analysis and classification. Diseases in maize diminish productivity, which is a major cause of economic damages in the agricultural business throughout the world. Researchers have previously utilized hand-crafted characteristics to classify images and identify leaf illnesses in Maize plants. With the advancement of deep learning, researchers can now significantly enhance the accuracy of object classification and identification. Using the "Corn or Maize Leaf Disease Dataset" from the Kaggle website, four forms of maize leaf diseases were investigated: blight, common rust, gray leaf spot, and healthy. The pictures obtained from these corn leaf illnesses are categorized using four deep convolutional neural network (CNN) models that have been pre-trained (GoogleNet, AlexNet, ResNet50 and VGG16). Accuracy, precision, specificity, recall, F-score, and time are the six metrics used to assess the performance of any transfer learning (TL) model. MATLAB programming software is used to design and train the TL models. The accuracy of each item in the dataset has been checked. It has been determined that GoogleNet, AlexNet, VGG16, and ResNet50 each have an accuracy of 98.57%, 98.81%, 99.05%, and 99.36%, respectively.</span>

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“…worked on face mask detection in public places [27][28]. Malaria infection detection was done in [29]. Multi-view video summarizing was done in [30].…”

Section: Related Workmentioning

confidence: 99%

Analysis of recovery cases of COVID19 of densely affected countries using LSTM deep learning model

Haque

Hoque²,

Paul

2023

Preprint

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The present world is badly affected by the novel coronavirus (COVID-19). Using medical kits to identify coronavirus-affected persons is very slow. What happens in the next, nobody knows. The world is facing an erratic problem and doesn't know what will happen in near future. This paper is trying to make a prognosis of the coronavirus recovery cases using LSTM(Long Short Term Memory). This work exploited data from 258 regions, their latitude and longitude, and the number of death in 403 days ranging from 22-01-2020 to 27-02-2021. Specifically, advanced deep learning-based algorithms known as the LSTM, play a great effect in extracting highly essential features for time series data (TSD) analysis. There are lots of methods which already use to analyze propagation prediction. The main task of this paper culminates in analyzing the spreading of Coronavirus across worldwide recovery cases using LSTM deep learning-based architectures.

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Deep Learning‐Based Image Classifier for Malaria Cell Detection

Cited by 29 publications

References 13 publications

MediNet: transfer learning approach with MediNet medical visual database

MediNet: transfer learning approach with MediNet medical visual database

Identifying corn leaves diseases by extensive use of transfer learning: a comparative study

Analysis of recovery cases of COVID19 of densely affected countries using LSTM deep learning model

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