Thin Blood Smear Images Research Articles

Evaluating Plasmodium falciparum automatic detection and parasitemia estimation: A comparative study on thin blood smear images.

Malaria is a deadly disease that is transmitted through mosquito bites. Microscopists use a microscope to examine thin blood smears at high magnification (1000x) to identify parasites in red blood cells (RBCs). Estimating parasitemia is essential in determining the severity of the Plasmodium falciparum infection and guiding treatment. However, this process is time-consuming, labor-intensive, and subject to variation, which can directly affect patient outcomes. In this retrospective study, we compared three methods for measuring parasitemia from a collection of anonymized thin blood smears of patients with Plasmodium falciparum obtained from the Clinical Department of Parasitology-Mycology, National Reference Center (NRC) for Malaria in Paris, France. We first analyzed the impact of the number of field images on parasitemia count using our framework, MALARIS, which features a top-classifier convolutional neural network (CNN). Additionally, we studied the variation between different microscopists using two manual techniques to demonstrate the need for a reliable and reproducible automated system. Finally, we included thin blood smear images from an additional 102 patients to compare the performance and correlation of our system with manual microscopy and flow cytometry. Our results showed strong correlations between the three methods, with a coefficient of determination between 0.87 and 0.92.

Exploring the Impact of Batch Size on Deep Learning Artificial Intelligence Models for Malaria Detection.

Introduction Malaria is a major public health concern, especially in developing countries. Malaria often presents with recurrent fever, malaise, and other nonspecific symptoms mistaken for influenza. Light microscopy of peripheral blood smears is considered the gold standard diagnostic test for malaria. Delays in malaria diagnosis can increase morbidity and mortality. Microscopy can be time-consuming and limited by skilled labor, infrastructure, and interobserver variability. Artificial intelligence (AI)-based tools for diagnostic screening can automate blood smear analysis without relying on a trained technician. Convolutional neural networks (CNN), deep learning neural networks that can identify visual patterns, are being explored for use in abnormality detection in medical images. A parameter that can be optimized in CNN models is the batch size or the number of images used during model training at once in one forward and backward pass. The choice of batch size in developing CNN-based malaria screening tools can affect model accuracy, training speed, and, ultimately, clinical usability. This study explores the impact of batch size on CNN model accuracy for malaria detection from thin blood smear images. Methods We used the publicly available "NIH-NLM-ThinBloodSmearsPf" dataset from the United States National Library of Medicine, consisting of blood smear images forPlasmodium falciparum. The collection consists of 13,779 "parasitized" and 13,779 "uninfected" single-cell images. We created four datasets containing all images, each with unique randomized subsets of images for model testing. Using Python, four identical 10-layer CNN models were developed and trained with varying batch sizes for 10 epochs against all datasets, resulting in 16 sets of outputs. Model prediction accuracy, training time, and F1-score,an accuracy metric used to quantify model performance, were collected. Results All models produced F1-scores of 94%-96%, with 10 of 16 instances producing F1-scores of 95%. After averaging all four dataset outputs by batch size, we observed that, as batch size increased from 16 to 128, the average combined false positives plus false negatives increased by 15.4% (130-150), and the average model F1-score accuracy decreased by 1% (95.3%-94.3%). The average training time also decreased by 28.11% (1,556-1,119 seconds). Conclusion In each dataset, we observe an approximately 1% decrease in F1-score as the batch size was increased. Clinically, a 1% deviation at the population level can create a relatively significant impact on outcomes. Results from this study suggest that smaller batch sizes could improve accuracy in models with similar layer complexity and datasets, potentially resulting in better clinical outcomes. Reduced memory requirement for training also means that model training can be achieved with more economical hardware. Our findings suggest that smaller batch sizes could be evaluated for improvements in accuracy to help develop an AI model that could screen thin blood smears for malaria.

An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images.

Malaria is a serious public health concern worldwide. Early and accurate diagnosis is essential for controlling the disease's spread and avoiding severe health complications. Manual examination of blood smear samples by skilled technicians is a time-consuming aspect of the conventional malaria diagnosis toolbox. Malaria persists in many parts of the world, emphasising the urgent need for sophisticated and automated diagnostic instruments to expedite the identification of infected cells, thereby facilitating timely treatment and reducing the risk of disease transmission. This study aims to introduce a more lightweight and quicker model-but with improved accuracy-for diagnosing malaria using a YOLOv4 (You Only Look Once v. 4) deep learning object detector. The YOLOv4 model is modified using direct layer pruning and backbone replacement. The primary objective of layer pruning is the removal and individual analysis of residual blocks within the C3, C4 and C5 (C3-C5) Res-block bodies of the backbone architecture's C3-C5 Res-block bodies. The CSP-DarkNet53 backbone is simultaneously replaced for enhanced feature extraction with a shallower ResNet50 network. The performance metrics of the models are compared and analysed. The modified models outperform the original YOLOv4 model. The YOLOv4-RC3_4 model with residual blocks pruned from the C3 and C4 Res-block body achieves the highest mean accuracy precision (mAP) of 90.70%. This mAP is > 9% higher than that of the original model, saving approximately 22% of the billion floating point operations (B-FLOPS) and 23MB in size. The findings indicate that the YOLOv4-RC3_4 model also performs better, with an increase of 9.27% in detecting the infected cells upon pruning the redundant layers from the C3 Res-block bodies of the CSP-DarkeNet53 backbone. The results of this study highlight the use of the YOLOv4 model for detecting infected red blood cells. Pruning the residual blocks from the Res-block bodies helps to determine which Res-block bodies contribute the most and least, respectively, to the model's performance. Our method has the potential to revolutionise malaria diagnosis and pave the way for novel deep learning-based bioinformatics solutions. Developing an effective and automated process for diagnosing malaria will considerably contribute to global efforts to combat this debilitating disease. We have shown that removing undesirable residual blocks can reduce the size of the model and its computational complexity without compromising its precision.

Application of Deep Learning in Clinical Settings for Detecting and Classifying Malaria Parasites in Thin Blood Smears.

Scarcity of annotated image data sets of thin blood smears makes expert-level differentiation among Plasmodium species challenging. Here, we aimed to establish a deep learning algorithm for identifying and classifying malaria parasites in thin blood smears and evaluate its performance and clinical prospect. You Only Look Once v7 was used as the backbone network for training the artificial intelligence algorithm model. The training, validation, and test sets for each malaria parasite category were randomly selected. A comprehensive analysis was performed on 12 708 thin blood smear images of various infective stages of 12 546 malaria parasites, including P falciparum, P vivax, P malariae, P ovale, P knowlesi, and P cynomolgi. Peripheral blood samples were obtained from 380 patients diagnosed with malaria. Additionally, blood samples from monkeys diagnosed with malaria were used to analyze P cynomolgi. The accuracy for detecting Plasmodium-infected blood cells was assessed through various evaluation metrics. The total time to identify 1116 malaria parasites was 13 seconds, with an average analysis time of 0.01 seconds for each parasite in the test set. The average precision was 0.902, with a recall and precision of infected erythrocytes of 96.0% and 94.9%, respectively. Sensitivity and specificity exceeded 96.8% and 99.3%, with an area under the receiver operating characteristic curve >0.999. The highest sensitivity (97.8%) and specificity (99.8%) were observed for trophozoites and merozoites. The algorithm can help facilitate the clinical and morphologic examination of malaria parasites.

Web-Based Application for Malaria Parasite Detection Using Thin-Blood Smear Images

Malaria is an infectious disease caused by the Plasmodium parasite. In 2019, there were 229 million cases of malaria with a death toll of 400.900. Malaria cases increased in 2020 to 241 million people with the death toll reaching 627,000. Malaria diagnosis which is carried out by observing the patient’s blood sample requires experts and if it is not done correctly, misdiagnosis can occur. Deep Learning can be used to help diagnose Malaria by classifying thin blood smear images. In this study, transfer learning techniques were used on the Convolutional Neural Network to speed up the model training process and get high accuracy. The architecture used for Transfer Learning is EfficientNetB0. The training model is embedded in a pythonbased web application which is then deployed on the Google App Engine platform. This is done so that it can be used by experts to help diagnose. The training model has a training accuracy of 0.9664, a training loss of 0.0937, a validation accuracy of 0.9734, and a validation loss of 0.0816. Prediction results on test data have an accuracy of 96.8% and an F1- score value of 0.968.

An automated malaria cells detection from thin blood smear images using deep learning.

Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films are examined annually. However, this method's effectiveness depends on the trained microscopist's skills. With the increasing interest in applying deep learning in malaria diagnosis, this study aims to determine the most suitable deep-learning object detection architecture and their applicability to detect and distinguish red blood cells as either malaria-infected or non-infected cells. The object detectors Yolov4, Faster R-CNN, and SSD 300 are trained with images infected by all five malaria parasites and from four stages of infection with 80/20 train and test data partition. The performance of object detectors is evaluated, and hyperparameters are optimized to select the best-performing model. The best-performing model was also assessed with an independent dataset to verify the models' ability to generalize in different domains. The results show that upon training, the Yolov4 model achieves a precision of 83%, recall of 95%, F1-score of 89%, and mean average precision of 93.87% at a threshold of 0.5. Conclusively, Yolov4 can act as an alternative in detecting the infected cells from whole thin blood smear images. Object detectors can complement a deep learning classification model in detecting infected cells since they eliminate the need to train on single-cell images and have been demonstrated to be more feasible for a different target domain.

Automated counting of white blood cells in thin blood smear images

Blood cell counting plays a crucial role in clinical diagnosis to evaluate the overall health condition of an individual. Traditionally, blood cells are manually counted using a hemocytometer; however, this task has been found to be time-consuming and error-prone. Recently, machine learning-based approaches have been employed to effectively automate counting tasks. In this work, the fifth version of the ‘you only look once’ (YOLOv5) object detection method was adopted to automatically detect and count white blood cells (WBCs) in porcine blood smear images. YOLOv5 was chosen because of its speed and accuracy. The dataset used in this study was collected specifically for this WBC counting task. Our experimental results exhibit the high speed and efficiency of YOLOv5 in detecting and counting WBCs, having obtained an accuracy of 89.25% and a mean average precision at 0.5 intersection over union threshold (mAP 0.5) of 99%.

Evaluating the Performance of Deep Learning Frameworks for Malaria Parasite Detection Using Microscopic Images of Peripheral Blood Smears.

Malaria is a significant health concern in many third-world countries, especially for pregnant women and young children. It accounted for about 229 million cases and 600,000 mortality globally in 2019. Hence, rapid and accurate detection is vital. This study is focused on achieving three goals. The first is to develop a deep learning framework capable of automating and accurately classifying malaria parasites using microscopic images of thin and thick peripheral blood smears. The second is to report which of the two peripheral blood smears is the most appropriate for use in accurately detecting malaria parasites in peripheral blood smears. Finally, we evaluate the performance of our proposed model with commonly used transfer learning models. We proposed a convolutional neural network capable of accurately predicting the presence of malaria parasites using microscopic images of thin and thick peripheral blood smears. Model evaluation was carried out using commonly used evaluation metrics, and the outcome proved satisfactory. The proposed model performed better when thick peripheral smears were used with accuracy, precision, and sensitivity of 96.97%, 97.00%, and 97.00%. Identifying the most appropriate peripheral blood smear is vital for improved accuracy, rapid smear preparation, and rapid diagnosis of patients, especially in regions where malaria is endemic.

Malaria parasites detection and identification using object detectors based on deep neural networks: a wide comparative analysis

ABSTRACT Malaria is an infectious disease transmitted by the bite of the female Anopheles mosquito, infected by Plasmodium spp. Early diagnosis and prompt and effective treatment are needed to avoid anaemia, organ failure, and death. Manual microscopy is the primary technique used for diagnosing malaria. However, this exam is labour-intensive and requires qualified personnel. In order to alleviate these difficulties, researchers are using computer vision concepts to detect and classify cells infected by the Plasmodium spp. This paper presents a comparative study of seven object detectors based on deep neural networks. We evaluated four versions of the You Only Look Once (YOLO), Single Shot MultiBox Detector, EfficientDet, and Faster R-CNN convolutional neural networks to detect and classify the malaria parasite in six different scenarios. We used four public image datasets, and the YOLOv5 model outperformed the other evaluated models. Their results had similar performance to the state-of-the-art works demonstrating the feasibility and effectiveness of YOLOv5 to detect and classify malaria parasites in thin blood smear images with high precision and sensitivity.

Malaria diagnosis using microscopic imaging

Malaria, a dangerous disease caused by Plasmodium, which is spread by being bitten by infected mosquitoes (Female Anopheles). It is crucial to diagnose malaria pathogens quickly and accurately at the right time. Traditional microscopy is commonly used in developing countries to diagnose malaria parasites, where pathologists examine the slide under a light microscope. However, in the case of traditional microscopy, requires more time and careful attention. Here, we have proposed a method to diagnose malaria based on computer vision. As a pre-processing stage, SAMF (Systematically Applied Mean Filter) algorithm is proposed that removes impulse noise from the corrupted malaria-infected images. Otsu method is used to obtain the binary version of images for cropping blood cells from complete image. 17 texture and color features were extracted from these cropped cells and these features were used to train Cubic SVM (Support Vector Machine) classifier. Hence a precise malaria diagnosis system was developed for detecting Plasmodium parasites, identifying their life stages and species using images of thin blood smears. A total of 348 images from CDC (Centre for Disease Control and Prevention) database were used to train and test the performance of the system.

A Proposed Model to eliminate the Confusion of Hematological Diseases in Thin Blood Smear by Using Deep Learning-Pretrained Model

This research aimed to developing and designing a model for resolving the confusion between hematology in a thin blood smear by means of a pre-defined deep learning model for detection and identification of hematological diseases in thin blood smear images for accurate diagnosis of the different diseases that leukemia and malaria were performed as a sample. There are catastrophic consequences that may lead to death as a result of a mistake in diagnosis and confusion in the knowledge of the disease in particular, especially in hematology, where another disease that was not originally found in the sample is identified for the similarity, which results in surgery and sometimes the administration of drugs in error. In this work, an image processing system was developed to identify patients with malaria and leukemia. The techniques in deep learning have been implemented where the CNN (Alexnet and Resnet50) image recognition model was applied to detect patterns and extract features of the different types of malaria and leukemia from the images. And that is through developing algorithms to distinguish between the two diseases, discovering the presence of similarities in the patterns of stages and the different types of malaria and leukemia in blood images, and reaching to solve the problem of confusion by training, verification, and testing using the mutual verification system that uses three folds. The system achieved an accuracy of 94.3% for Resnet50 and 92.3 for Alexnet in detecting and classifying the types and stages of the two diseases (malaria and leukemia). And 100% to distinguish between them.

Real-life evaluation of deep learning models trained on two datasets for Plasmodium falciparum detection with thin blood smear images at 500x magnification

Malaria is a fatal disease transmitted by bites from mosquito-type vectors. Biologists examined blood smears under a microscope at high magnification (1000 × ) to identify the presence of parasites in red blood cells (RBCs). Such an examination is laborious and time-consuming. Moreover, microscopists sometimes have difficulty identifying parasitized RBCs due to a lack of skill or practice. Deep learning, especially convolutional neural networks (CNNs) applied for malaria diagnosis, are able to identify complex features of a large number of medical images.The proposed work focuses on the construction of a dataset of blood components images representative of the diagnostic reality captured from 202 patients at 500x magnification. We evaluated through a cross-validation study different deep learning networks for the classification of Plasmodium falciparum-infected RBCs and uninfected blood components. These models include a custom-built CNN, VGG-19, ResNet-50 and EfficientNet-B7. In addition, we conducted the same experiments on a public dataset and compared the performance of the resultant models through a patient-level inference including 200 extra patients. The models trained on our dataset show better performance in terms of generalization and achieved better accuracy, sensitivity and specificity scores of 99.7%, 77.9% and 99.8%, respectively.

Automated segmentation of leukocyte from hematological images\u2014a study using various CNN schemes

Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.

Malaria Parasite Detection using Efficient Neural Ensembles

Caused by the bite of the Anopheles mosquito infected with the parasite of genus Plasmodium, malaria has remained a major burden towards healthcare for years with an approximate 400,000 deaths reported globally every year. The traditional diagnosis process for malaria involves an examination of the blood smear slide under the microscope. This process is not only time consuming but also requires pathologists to be highly skilled in their work. Timely diagnosis and availability of robust diagnostic facilities and skilled laboratory technicians are very much vital to reduce the mortality rate. This study aims to build a robust system by applying deep learning techniques such as transfer learning and snapshot ensembling to automate the detection of the parasite in the thin blood smear images. All the models were evaluated against the following metrics - F1 score, Accuracy, Precision, Recall, Mathews Correlation Coefficient (MCC), Area Under the Receiver Operating Characteristics (AUC-ROC) and the Area under the Precision Recall curve (AUC-PR). The snapshot ensembling model created by combining the snapshots of the EfficientNet-B0 pre-trained model outperformed every other model achieving a f1 score - 99.37%, precision - 99.52% and recall - 99.23%. The results show the potential of model ensembles which combine the predictive power of multiple weal models to create a single efficient model that is better equipped to handle the real world data. The GradCAM experiment displayed the gradient activation maps of the last convolution layer to visually explicate where and what a model sees in an image to classify them into a particular class. The models in this study correctly activate the stained parasitic region of interest in the thin blood smear images. Such visuals make the model more transparent, explainable, and trustworthy which are very much essential for deploying AI based models in the healthcare network.

Malaria Parasite Detection using Efficient Neural Ensembles

Caused by the bite of the Anopheles mosquito infected with the parasite of genus Plasmodium, malaria has remained a major burden towards healthcare for years with an approximate 400,000 deaths reported globally every year. The traditional diagnosis process for malaria involves an examination of the blood smear slide under the microscope. This process is not only time consuming but also requires pathologists to be highly skilled in their work. Timely diagnosis and availability of robust diagnostic facilities and skilled laboratory technicians are very much vital to reduce the mortality rate. This study aims to build a robust system by applying deep learning techniques such as transfer learning and snapshot ensembling to automate the detection of the parasite in the thin blood smear images. All the models were evaluated against the following metrics - F1 score, Accuracy, Precision, Recall, Mathews Correlation Coefficient (MCC), Area Under the Receiver Operating Characteristics (AUC-ROC) and the Area under the Precision Recall curve (AUC-PR). The snapshot ensembling model created by combining the snapshots of the EfficientNet-B0 pre-trained model outperformed every other model achieving a f1 score - 99.37%, precision - 99.52% and recall - 99.23%. The results show the potential of model ensembles which combine the predictive power of multiple weal models to create a single efficient model that is better equipped to handle the real world data. The GradCAM experiment displayed the gradient activation maps of the last convolution layer to visually explicate where and what a model sees in an image to classify them into a particular class. The models in this study correctly activate the stained parasitic region of interest in the thin blood smear images. Such visuals make the model more transparent, explainable, and trustworthy which are very much essential for deploying AI based models in the healthcare network.

Malaria detection through digital microscopic imaging using Deep Greedy Network with transfer learning.

Purpose: In conventional diagnosis, the visual inspection of the malaria parasite Plasmodium falciparum in infected red blood cells under a microscope, is done manually by pathologists, which is both laborious and error-prone. Recent studies on automating this process have been conducted using artificial intelligence and feature selection of positional and morphological features from blood smear cell images using convolutional neural network (CNN). However, most deep CNN models do not perform well as per the expectation on small datasets. Approach: In this context, we propose a comprehensive computer-aided diagnosis scheme for automating the detection of malaria parasites in thin blood smear images using deep CNN, where transfer learning is used for optimizing the feature selection process. As an extra layer of security, layer embeddings are extracted from the intermediate convolutional layers using the feature matrix to cross-check the selection of features in the intermediate layers. The proposal includes the utilization of the ResNet 152 model integrated with the Deep Greedy Network for training, which produces an enhanced quality of prediction. Results: The performance of the proposed hybrid model has been evaluated concerning the evaluation metrics such as accuracy, precision, recall, specificity, and F1-score, which has been further compared with the pre-existing deep learning algorithms. Conclusions: The comparative analysis of the results reported based on the accuracy metrics demonstrates promising outcomes concerning the other models. Lastly, the embedding extraction from the intermediate hidden layers and their visual analysis also provides an opportunity for manual verification of the performance of the trained model.

Automated detection of plasmodium species using Machine-Learning technique

Background: Malaria is one of the widespread mosquito-borne diseases. The WHO report of year 2018 confirms a recorded disease rate of 219 million and a death rate of 435,000. Accurate diagnosis of malaria infection is essential for appropriate management of cases. WHO's Global Technical Strategy for Malaria 2016–2030 recommends that surveillance strategies should be integrated as core interventions to accelerate progress towards elimination. The proposed work aims to develop a Machine-Learning (ML) system for the assessment and classification of the parasite existing in the stained thin blood smear images recorded with a digital microscope. This work implements a multi-class classifier to categorize the plasmodium according to its shape and texture features. Methods and materials: In this work, an automated system is proposed to extract, evaluate and classify the plasmodium species available in the considered test images. The proposed system initially implements a multi-level thresholding to enhance the image and plasmodium in enhanced image is extracted using morphological segmentation, later the texture/shape features are extracted and finally a multi class classifier is implemented to categorize the test images based on the parasite and its growth phase. In order to attain better classification accuracy, this work implements prevailing feature selection and multi-class classification based on Support-Vector-Machine (SVM). Results: The developed system was tested on 2000 numbers of RGB scale thin blood smear pictures of size 512 x 512 pixels and the results obtained in the proposed technique was evaluated to validate the performance of proposed ML system. The results of this study provides an overall classification accuracy of 97.05% with the SVM and relative assessment with the comparable procedures in the literature also verifies that, ML technique offers superior values of accuracy, precision, sensitivity, specificity and F1score. Conclusion: This work implements a Machine-Learning tool to asses and classifies the plasmodium species using the stained thin blood smear images. The outcome of the proposed tool confirms that, it works well on considered microscopic image database and helps to attain better classification accuracy. In future, the proposed approach can be used to examine the clinical grade thin blood smear images of malaria infected patients.

Malaria Screener: a smartphone application for automated malaria screening

BackgroundLight microscopy is often used for malaria diagnosis in the field. However, it is time-consuming and quality of the results depends heavily on the skill of microscopists. Automating malaria light microscopy is a promising solution, but it still remains a challenge and an active area of research. Current tools are often expensive and involve sophisticated hardware components, which makes it hard to deploy them in resource-limited areas.ResultsWe designed an Android mobile application called Malaria Screener, which makes smartphones an affordable yet effective solution for automated malaria light microscopy. The mobile app utilizes high-resolution cameras and computing power of modern smartphones to screen both thin and thick blood smear images for P. falciparum parasites. Malaria Screener combines image acquisition, smear image analysis, and result visualization in its slide screening process, and is equipped with a database to provide easy access to the acquired data.ConclusionMalaria Screener makes the screening process faster, more consistent, and less dependent on human expertise. The app is modular, allowing other research groups to integrate their methods and models for image processing and machine learning, while acquiring and analyzing their data.

Analyzing Malaria Disease Using Effective Deep Learning Approach.

Medical tools used to bolster decision-making by medical specialists who offer malaria treatment include image processing equipment and a computer-aided diagnostic system. Malaria images can be employed to identify and detect malaria using these methods, in order to monitor the symptoms of malaria patients, although there may be atypical cases that need more time for an assessment. This research used 7000 images of Xception, Inception-V3, ResNet-50, NasNetMobile, VGG-16 and AlexNet models for verification and analysis. These are prevalent models that classify the image precision and use a rotational method to improve the performance of validation and the training dataset with convolutional neural network models. Xception, using the state of the art activation function (Mish) and optimizer (Nadam), improved the effectiveness, as found by the outcomes of the convolutional neural model evaluation of these models for classifying the malaria disease from thin blood smear images. In terms of the performance, recall, accuracy, precision, and F1 measure, a combined score of 99.28% was achieved. Consequently, 10% of all non-dataset training and testing images were evaluated utilizing this pattern. Notable aspects for the improvement of a computer-aided diagnostic to produce an optimum malaria detection approach have been found, supported by a 98.86% accuracy level.

Using autoencoders as a weight initialization method on deep neural networks for disease detection

BackgroundAs of today, cancer is still one of the most prevalent and high-mortality diseases, summing more than 9 million deaths in 2018. This has motivated researchers to study the application of machine learning-based solutions for cancer detection to accelerate its diagnosis and help its prevention. Among several approaches, one is to automatically classify tumor samples through their gene expression analysis.MethodsIn this work, we aim to distinguish five different types of cancer through RNA-Seq datasets: thyroid, skin, stomach, breast, and lung. To do so, we have adopted a previously described methodology, with which we compare the performance of 3 different autoencoders (AEs) used as a deep neural network weight initialization technique. Our experiments consist in assessing two different approaches when training the classification model — fixing the weights after pre-training the AEs, or allowing fine-tuning of the entire network — and two different strategies for embedding the AEs into the classification network, namely by only importing the encoding layers, or by inserting the complete AE. We then study how varying the number of layers in the first strategy, the AEs latent vector dimension, and the imputation technique in the data preprocessing step impacts the network’s overall classification performance. Finally, with the goal of assessing how well does this pipeline generalize, we apply the same methodology to two additional datasets that include features extracted from images of malaria thin blood smears, and breast masses cell nuclei. We also discard the possibility of overfitting by using held-out test sets in the images datasets.ResultsThe methodology attained good overall results for both RNA-Seq and image extracted data. We outperformed the established baseline for all the considered datasets, achieving an average F1 score of 99.03, 89.95, and 98.84 and an MCC of 0.99, 0.84, and 0.98, for the RNA-Seq (when detecting thyroid cancer), the Malaria, and the Wisconsin Breast Cancer data, respectively.ConclusionsWe observed that the approach of fine-tuning the weights of the top layers imported from the AE reached higher results, for all the presented experiences, and all the considered datasets. We outperformed all the previous reported results when comparing to the established baselines.