Optimizing Deep Convolutional Neural Network With Fine-Tuning and Data Augmentation For Covid-19 Prediction

2021 ◽  
Author(s):  
Abdusy Syarif ◽  
Novi Azman ◽  
Ernawati Sinaga
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning

Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

2021 ◽  
pp. 1-10
Author(s):  
Gayatri Pattnaik ◽  
Vimal K. Shrivastava ◽  
K. Parvathi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Tomato Plants ◽  
Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

scholarly journals "LeukNet" - A Model of Convolutional Neural Network for the Diagnosis of Leukemia

2020 ◽  
Author(s):  
Luis H. S. Vogado ◽  
Rodrigo M. S. Veras ◽  
Kelson R. T. Aires
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cross Validation ◽  
Data Augmentation ◽  
Fine Tuning ◽  
Current State ◽  
Leukemia Diagnosis ◽  
New Guidelines ◽  
Texture Contrast ◽  
Tuning Methods

Leukemia is a disorder that affects the bone marrow, causing uncontrolled production of leukocytes, impairing the transport of oxygen and causing blood coagulation problems. In this article, we propose a new computational tool, named LeukNet, a Convolutional Neural Network (CNN) architecture based on the VGG-16 convolutional blocks, to facilitate the leukemia diagnosis from blood smear images. We evaluated different architectures and fine-tuning methods using 18 datasets containing 3536 images with distinct characteristics of color, texture, contrast, and resolution. Additionally, data augmentation operations were applied to increase the training set by up to 20 times. The k-fold cross-validation (k = 5) results achieved 98.28% of accuracy. A cross-dataset validation technique, named LeaveOne-Dataset-Out Cross-Validation (LODOCV), is also proposed to evaluate the developed model’s generalization capability. The accuracy of using LODOCV on the ALL-IDB 1, ALL-IDB 2, and UFG datasets was 97.04%, 82.46%, and 70.24%, respectively, overcoming the current state-of-the-art results and offering new guidelines for image-based computer-aided diagnosis (CAD) systems in this area.

scholarly journals A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images

2020 ◽  
Vol 12 (6) ◽  
pp. 1015 ◽  
Author(s):  
Kan Zeng ◽  
Yixiao Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Oil Spill ◽  
Data Augmentation ◽  
Classification Performance ◽  
Deep Convolutional Neural Network ◽  
Convolutional Network ◽  
Data Set ◽  
Oil Spill Detection ◽  
Processing Framework

Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level.

scholarly journals A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Lei Si ◽  
Xiangxiang Xiong ◽  
Zhongbin Wang ◽  
Chao Tan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Deep Convolutional Neural Network ◽  
Accurate Identification ◽  
Experiment System ◽  
Speed Up ◽  
Novel Method ◽  
Coal Rock ◽  
Mining Face

Accurate identification of the distribution of coal seam is a prerequisite for realizing intelligent mining of shearer. This paper presents a novel method for identifying coal and rock based on a deep convolutional neural network (CNN). Three regularization methods are introduced in this paper to solve the overfitting problem of CNN and speed up the convergence: dropout, weight regularization, and batch normalization. Then the coal-rock image information is enriched by means of data augmentation, which significantly improves the performance. The shearer cutting coal-rock experiment system is designed to collect more real coal-rock images, and some experiments are provided. The experiment results indicate that the network we designed has better performance in identifying the coal-rock images.

scholarly journals Skin Lesion Classification With Deep Convolutional Neural Network: Process Development and Validation

10.2196/18438 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e18438
Author(s):  
Arnab Ray ◽  
Aman Gupta ◽  
Amutha Al
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Process Development ◽  
Early Stage ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Network Process ◽  
Lesion Classification

Background Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. Objective We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. Methods We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. Results We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. Conclusions Based on our research, we conclude that deep learning algorithms are highly suitable for classifying skin cancer images.

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