DIAGNOSIS AND CLASSIFICATION ASSISTANCE FROM LYMPHOMA MICROSCOPIC IMAGES USING DEEP LEARNING

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

scholarly journals Deep learning approach to describe and classify fungi microscopic images

PLoS ONE ◽  
2020 ◽  
Vol 15 (6) ◽  
pp. e0234806 ◽  
Author(s):  
Bartosz Zieliński ◽  
Agnieszka Sroka-Oleksiak ◽  
Dawid Rymarczyk ◽  
Adam Piekarczyk ◽  
Monika Brzychczy-Włoch
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Microscopic Images

scholarly journals Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3068
Author(s):  
Soumaya Dghim ◽  
Carlos M. Travieso-González ◽  
Radim Burget
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
The Other ◽  
Support Vector ◽  
Learning Approaches ◽  
Microscopic Images ◽  
Trained Neural Network ◽  
Nosema Disease

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%.

An Extensive Review on Machine Learning and Deep Learning Based Cervical Cancer Diagnosis and Classification Models

2020 ◽  
Vol 17 (12) ◽  
pp. 5438-5446
Author(s):  
C. Suguna ◽  
S. P. Balamurugan
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Deep Learning ◽  
Comparative Study ◽  
Cancer Diagnosis ◽  
Pap Smear ◽  
Classification Models ◽  
Extensive Review ◽  
Diagnosis And Classification ◽  
Automated Machine Learning

Cervical cancer is a commonly occurring deadliest disease among women, which needs earlier diagnosis to reduce the prevalence. Pap-smear is considered as a widely employed technique to screen and diagnose cervical cancer. Since classical manual screening techniques are inefficient in the identification of cervical cancer, several research works have been started to develop automated machine learning (ML) and deep learning (DL) tools for cervical cancer diagnosis. This paper surveys the recent works made on cervical cancer diagnosis and classification. The recently presently ML and DL models for cervical cancer diagnosis and classification has been reviewed in detail. Besides, segmentation techniques developed for cervical cancer diagnosis also surveyed. At the end of the survey, a brief comparative study has been carried out to identify the significance of the reviewed methods.

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

2020 ◽  
pp. 807-813
Author(s):  
Priyadarshini Patil ◽  
Prashant Narayankar ◽  
Deepa Mulimani ◽  
Mayur Patil
Keyword(s):  
Deep Learning ◽  
Learning Environment ◽  
Plant Diseases ◽  
Microscopic Images

scholarly journals Deep Learning Based Depthwise Separable Model For Effective Diagnosis And Classification of Lung Ct Images

2019 ◽  
Vol 9 (1) ◽  
pp. 1808-1819 ◽  
Keyword(s):  
Lung Cancer ◽  
Feature Extraction ◽  
Deep Learning ◽  
Noise Removal ◽  
Ct Images ◽  
Classification Model ◽  
Diagnosis And Classification ◽  
Effective Diagnosis ◽  
Lung Ct

Lung cancer is a serious illness which leads to increased mortality rate globally. The identification of lung cancer at the beginning stage is the probable method of improving the survival rate of the patients. Generally, Computed Tomography (CT) scan is applied for finding the location of the tumor and determines the stage of cancer. Existing works has presented an effective diagnosis classification model for CT lung images. This paper designs an effective diagnosis and classification model for CT lung images. The presented model involves different stages namely pre-processing, segmentation, feature extraction and classification. The initial stage includes an adaptive histogram based equalization (AHE) model for image enhancement and bilateral filtering (BF) model for noise removal. The pre-processed images are fed into the second stage of watershed segmentation model for effectively segment the images. Then, a deep learning based Xception model is applied for prominent feature extraction and the classification takes place by the use of logistic regression (LR) classifier. A comprehensive simulation is carried out to ensure the effective classification of the lung CT images using a benchmark dataset. The outcome implied the outstanding performance of the presented model on the applied test images.

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

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

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

2021 ◽  
Author(s):  
Ziheng Yang ◽  
Halim Benhabiles ◽  
Karim Hammoudi ◽  
Feryal Windal ◽  
Ruiwen He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Malaria Diagnosis ◽  
Microscopic Images ◽  
Human Malaria

Deep learning for diagnosis and classification of faults in industrial rotating machinery

2021 ◽  
Vol 153 ◽  
pp. 107060
Author(s):  
Roberto M. Souza ◽  
Erick G.S. Nascimento ◽  
Ubatan A. Miranda ◽  
Wenisten J.D. Silva ◽  
Herman A. Lepikson
Keyword(s):  
Deep Learning ◽  
Rotating Machinery ◽  
Diagnosis And Classification
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