A generalized deep learning-based framework for assistance to the human malaria diagnosis from microscopic images

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

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Using Microscopic Images to Predict Plant Diseases in a Deep Learning Environment

ICT for Competitive Strategies ◽

10.1201/9781003052098-85 ◽

2020 ◽

pp. 807-813

Author(s):

Priyadarshini Patil ◽

Prashant Narayankar ◽

Deepa Mulimani ◽

Mayur Patil

Keyword(s):

Deep Learning ◽

Learning Environment ◽

Plant Diseases ◽

Microscopic Images

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Instance Segmentation of 2D Label-Free Microscopic Images using Deep Learning

10.1109/niles53778.2021.9600522 ◽

2021 ◽

Author(s):

Basma A. Mohamed ◽

Lamees N. Mahmoud ◽

Walid Al-Atabany ◽

Nancy M. Salem

Keyword(s):

Deep Learning ◽

Label Free ◽

Microscopic Images ◽

Instance Segmentation

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Deep learning based classification for metastasis of hepatocellular carcinoma with microscopic images

Medical Imaging 2019: Image Processing ◽

10.1117/12.2512214 ◽

2019 ◽

Author(s):

Hui Meng ◽

Yuan Gao ◽

Kun Wang ◽

Jie Tian

Keyword(s):

Hepatocellular Carcinoma ◽

Deep Learning ◽

Microscopic Images

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DIAGNOSIS AND CLASSIFICATION ASSISTANCE FROM LYMPHOMA MICROSCOPIC IMAGES USING DEEP LEARNING

Hematological Oncology ◽

10.1002/hon.97_2629 ◽

2019 ◽

Vol 37 ◽

pp. 138-138

Author(s):

P. Brousset ◽

C. Syrykh ◽

A. Abreu ◽

N. Amara ◽

C. Laurent

Keyword(s):

Deep Learning ◽

Microscopic Images ◽

Diagnosis And Classification

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A deep learning approach for patch-based disease diagnosis from microscopic images

Classification Techniques for Medical Image Analysis and Computer Aided Diagnosis ◽

10.1016/b978-0-12-818004-4.00005-4 ◽

2019 ◽

pp. 109-127 ◽

Cited By ~ 1

Author(s):

Anson Simon ◽

Ravi Vinayakumar ◽

Viswanathan Sowmya ◽

Kutti Padannayil Soman ◽

Ennappadam Anathanarayanan A. Gopalakrishnan

Keyword(s):

Deep Learning ◽

Disease Diagnosis ◽

Learning Approach ◽

Microscopic Images

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Malaria Parasite Detection in Thick Blood Smear Microscopic Images Using Modified YOLOV3 and YOLOV4 Models

10.21203/rs.3.rs-74079/v1 ◽

2020 ◽

Author(s):

Fetulhak Abdurahman ◽

Kinde Fante Anlay ◽

Mohammed Aliy

Keyword(s):

Object Detection ◽

Malaria Parasite ◽

Clustering Algorithm ◽

State Of The Art ◽

Malaria Diagnosis ◽

Learning Ability ◽

Background Information ◽

Small Object ◽

Microscopic Images ◽

Resource Setting

Abstract Background Information: Manual microscopic examination is still the "golden standard" for malaria diagnosis. The challenge in the manual microscopy is the fact that its accuracy, consistency and speed of diagnosis depends on the skill of the laboratory technician. It is difficult to get highly skilled laboratory technicians in the remote areas of developing countries. In order to alleviate this problem, in this paper, we propose and investigate the state-of-the-art one-stage and two-stage object detection algorithms for automated malaria parasite screening from thick blood slides. Methods: YOLOV3 and YOLOV4 are state-of-the-art object detectors both in terms of accuracy and speed; however, they are not optimized for the detection of small objects such as malaria parasite in microscopic images. To deal with these challenges, we have modified YOLOV3 and YOLOV4 models by increasing the feature scale and by adding more detection layers, without notably decreasing their detection speed. We have proposed one modified YOLOV4 model, called YOLOV4-MOD and two modified models for YOLOV3, which are called YOLOV3-MOD1 and YOLOV3-MOD2. In addition, we have generated new anchor box scales and sizes by using the K-means clustering algorithm to exploit small object detection learning ability of the models.Results: The proposed modified YOLOV3 and YOLOV4 algorithms are evaluated on publicly available malaria dataset and achieve state-of-the-art accuracy by exceeding the performance of their original versions, Faster R-CNN and SSD in terms of mean average precision (mAP), recall, precision, F1 score, and average IOU. For 608 x 608 input resolution YOLOV4-MOD achieves the best detection performance among all the other models with mAP of 96.32%. For the same input resolution YOLOV3-MOD2 and YOLOV3-MOD1 achieved mAP of 96.14% and 95.46% respectively. Conclusions: Th experimental results demonstrate that the performance of the proposed modified YOLOV3 and YOLOV4 models are reliable to be applied for detection of malaria parasite from images that can be captured by smartphone camera over the microscope eyepiece. The proposed system can be easily deployed in low-resource setting and it can save lives.

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A Deep Learning-Based Method for Automatic Assessment of Stomatal Index in Wheat Microscopic Images of Leaf Epidermis

Frontiers in Plant Science ◽

10.3389/fpls.2021.716784 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chuancheng Zhu ◽

Yusong Hu ◽

Hude Mao ◽

Shumin Li ◽

Fangfang Li ◽

...

Keyword(s):

Deep Learning ◽

Stomatal Density ◽

Automatic Measurement ◽

Epidermal Cells ◽

Leaf Epidermis ◽

Stomatal Index ◽

Automatic Assessment ◽

Microscopic Images ◽

Index Measurement ◽

Diagnostic Characteristics

The stomatal index of the leaf is the ratio of the number of stomata to the total number of stomata and epidermal cells. Comparing with the stomatal density, the stomatal index is relatively constant in environmental conditions and the age of the leaf and, therefore, of diagnostic characteristics for a given genotype or species. Traditional assessment methods involve manual counting of the number of stomata and epidermal cells in microphotographs, which is labor-intensive and time-consuming. Although several automatic measurement algorithms of stomatal density have been proposed, no stomatal index pipelines are currently available. The main aim of this research is to develop an automated stomatal index measurement pipeline. The proposed method employed Faster regions with convolutional neural networks (R-CNN) and U-Net and image-processing techniques to count stomata and epidermal cells, and subsequently calculate the stomatal index. To improve the labeling speed, a semi-automatic strategy was employed for epidermal cell annotation in each micrograph. Benchmarking the pipeline on 1,000 microscopic images of leaf epidermis in the wheat dataset (Triticum aestivum L.), the average counting accuracies of 98.03 and 95.03% for stomata and epidermal cells, respectively, and the final measurement accuracy of the stomatal index of 95.35% was achieved. R2 values between automatic and manual measurement of stomata, epidermal cells, and stomatal index were 0.995, 0.983, and 0.895, respectively. The average running time (ART) for the entire pipeline could be as short as 0.32 s per microphotograph. The proposed pipeline also achieved a good transferability on the other families of the plant using transfer learning, with the mean counting accuracies of 94.36 and 91.13% for stomata and epidermal cells and the stomatal index accuracy of 89.38% in seven families of the plant. The pipeline is an automatic, rapid, and accurate tool for the stomatal index measurement, enabling high-throughput phenotyping, and facilitating further understanding of the stomatal and epidermal development for the plant physiology community. To the best of our knowledge, this is the first deep learning-based microphotograph analysis pipeline for stomatal index assessment.

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Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT

Computational and Mathematical Methods in Medicine ◽

10.1155/2022/9934144 ◽

2022 ◽

Vol 2022 ◽

pp. 1-8

Author(s):

Mohammad Manthouri ◽

Zhila Aghajari ◽

Sheida Safary

Keyword(s):

Neural Network ◽

Deep Learning ◽

Blood Cells ◽

Feature Detection ◽

White Blood Cells ◽

Processing System ◽

Health Economy ◽

Microscopic Images ◽

Global Issues ◽

Different Types

Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intelligence (AI) techniques are now widely used to detect these blood cells and explore their structures. In recent years, deep learning architectures have been utilized as they are powerful tools for big data analysis. In this work, we are presenting a deep neural network for processing of microscopic images of blood cells. Processing these images is particularly important as white blood cells and their structures are being used to diagnose different diseases. In this research, we design and implement a reliable processing system for blood samples and classify five different types of white blood cells in microscopic images. We use the Gram-Schmidt algorithm for segmentation purposes. For the classification of different types of white blood cells, we combine Scale-Invariant Feature Transform (SIFT) feature detection technique with a deep convolutional neural network. To evaluate our work, we tested our method on LISC and WBCis databases. We achieved 95.84% and 97.33% accuracy of segmentation for these data sets, respectively. Our work illustrates that deep learning models can be promising in designing and developing a reliable system for microscopic image processing.

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