scholarly journals A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2364
Author(s):  
Ali Mirzazadeh ◽  
Afshin Azizi ◽  
Yousef Abbaspour-Gilandeh ◽  
José Luis Hernández-Hernández ◽  
Mario Hernández-Hernández ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Vital Role ◽  
Crop Damage ◽  
Deep Convolutional Neural Networks ◽  
Agriculture Sector ◽  
Novel Technique ◽  
Deep Learning Algorithm

Estimation of crop damage plays a vital role in the management of fields in the agriculture sector. An accurate measure of it provides key guidance to support agricultural decision-making systems. The objective of the study was to propose a novel technique for classifying damaged crops based on a state-of-the-art deep learning algorithm. To this end, a dataset of rapeseed field images was gathered from the field after birds’ attacks. The dataset consisted of three classes including undamaged, partially damaged, and fully damaged crops. Vgg16 and Res-Net50 as pre-trained deep convolutional neural networks were used to classify these classes. The overall classification accuracy reached 93.7% and 98.2% for the Vgg16 and the ResNet50 algorithms, respectively. The results indicated that a deep neural network has a high ability in distinguishing and categorizing different image-based datasets of rapeseed. The findings also revealed a great potential of deep learning-based models to classify other damaged crops.

Ensemble-based deep meta learning for medical image segmentation

2021 ◽  
pp. 1-7
Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
The State ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Meta Learning ◽  
Small Set

Deep learning methods have led to a state of the art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders to collaborate. However, limited labelled data set limits the deep learning algorithm to generalize for one domain into another. To handle the problem, meta-learning helps to learn from a small set of data. We proposed a meta learning-based image segmentation model that combines the learning of the state-of-the-art model and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segments part and remove noise from the new test image. The proposed model can achieve 0.94 precision and 0.92 recall. The ability to increase 3.3% among the state-of-the-art algorithms.

scholarly journals Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm

2020 ◽  
Vol 10 (4) ◽  
pp. 213 ◽  
Author(s):  
Ki-Sun Lee ◽  
Jae Young Kim ◽  
Eun-tae Jeon ◽  
Won Suk Choi ◽  
Nan Hee Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Learning Algorithm ◽  
Fine Tuning ◽  
Deep Convolutional Neural Networks ◽  
X Ray ◽  
Backbone Network ◽  
Deep Learning Algorithm ◽  
Chest X Ray

According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.

Image De-Noising Using Deep Learning

2014 ◽  
Vol 641-642 ◽  
pp. 1287-1290
Author(s):  
Lan Zhang ◽  
Yu Feng Nie ◽  
Zhen Hai Wang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Input Data ◽  
Deep Neural Network ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Large Data ◽  
Image Database ◽  
Learning Problems ◽  
Deep Learning Algorithm

Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.

scholarly journals A DEEP LEARNING ARCHITECTURE FOR BATCH-MODE FULLY AUTOMATED FIELD BOUNDARY DETECTION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1009-1016
Author(s):  
L. Meyer ◽  
F. Lemarchand ◽  
P. Sidiropoulos
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Boundary Detection ◽  
Detection Problem ◽  
Field Boundary ◽  
Batch Mode ◽  
Deep Learning Algorithm ◽  
Positive Results ◽  
Sentinel 2

Abstract. The accurate split of large areas of land into discrete fields is a crucial step for several agriculture-related remote sensing pipelines. This work aims to fully automate this tedious and resource-demanding process using a state-of-the-art deep learning algorithm with only a single Sentinel-2 image as input. The Mask R-CNN, which has forged its success upon instance segmentation for objects from everyday life, is adapted for the field boundary detection problem. Such model automatically generates closed geometries without any heavy post-processing. When tested with satellite imagery from Denmark, this tailored model correctly predicts field boundaries with an overall accuracy of 0.79. Besides, it demonstrates a robust knowledge generalisation with positive results over different geographies, as it gets an overall accuracy of 0.71 when used over areas in France.

scholarly journals DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Abdul Wahab ◽  
Hilal Tayara ◽  
Zhenyu Xuan ◽  
Kil To Chong
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Dna Sequences ◽  
Area Under Curve ◽  
Cross Validation ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Deep Learning Algorithm ◽  
Fold Cross Validation ◽  
Genome Dataset

AbstractN4-methylcytosine is a biochemical alteration of DNA that affects the genetic operations without modifying the DNA nucleotides such as gene expression, genomic imprinting, chromosome stability, and the development of the cell. In the proposed work, a computational model, 4mCNLP-Deep, used the word embedding approach as a vector formulation by exploiting deep learning based CNN algorithm to predict 4mC and non-4mC sites on the C.elegans genome dataset. Diversity of ranges employed for the experimental such as corpus k-mer and k-fold cross-validation to obtain the prevailing capabilities. The 4mCNLP-Deep outperform from the state-of-the-art predictor by achieving the results in five evaluation metrics by following; Accuracy (ACC) as 0.9354, Mathew’s correlation coefficient (MCC) as 0.8608, Specificity (Sp) as 0.89.96, Sensitivity (Sn) as 0.9563, and Area under curve (AUC) as 0.9731 by using 3-mer corpus word2vec and 3-fold cross-validation and attained the increment of 1.1%, 0.6%, 0.58%, 0.77%, and 4.89%, respectively. At last, we developed the online webserver http://nsclbio.jbnu.ac.kr/tools/4mCNLP-Deep/, for the experimental researchers to get the results easily.

scholarly journals Multi-run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers

2021 ◽  
Author(s):  
Abdullah Al Mamun ◽  
Raihanul Bari Tanvir ◽  
Masrur Sobhan ◽  
Kalai Mathee ◽  
Giri Narasimhan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Target Genes ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Expression Profiles ◽  
Risk Groups ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Stable Set ◽  
Deep Learning Algorithm

Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that leads to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. To discover the critical lncRNAs that can identify the origin of different cancers, we proposed to use the state-of-the-art deep learning algorithm Concreate Autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We proposed a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, a total of 4,768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to the final list of key lncRNAs, which are capable of identifying 12 different cancers. Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study discovered a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability in differentiating high- and low-risk groups of patients in different cancers. The proposed mrCAE outperformed the standard autoencoder, which selects the latent features and is thought to be the upper limit in dimension reduction. Since the proposed mrCAE selects actual features and outperformed AE, it has the potential to provide information that can be used for precision medicine, such as identifying prognostic lncRNAs (this work) and mRNAs, miRNAs, and DNA methylated genes (future work) for different cancers.

scholarly journals Skin Lesions Classification Using Deep Learning Techniques: Review

2021 ◽  
pp. 1-22
Author(s):  
Omar Sedqi Kareem ◽  
Adnan Mohsin Abdulazee ◽  
Diyar Qader Zeebaree
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Learning Algorithm ◽  
Skin Lesions ◽  
Disease Diagnosis ◽  
Deep Convolutional Neural Networks ◽  
Deep Learning Algorithm ◽  
The Usa ◽  
Effective Models

Skin cancer is a significant health problem. More than 123,000 new cases per year are recorded. Melanoma is the most popular type of skin cancer, leading to more than 9000 deaths annually in the USA. Skin disease diagnosis is getting difficult due to visual similarities. While Melanoma is the most common form of skin cancer, other pathology types are also fatal. Automatic melanoma screening systems will be useful in identifying those skin cancers more appropriately. Advances in technology and growth in computational capabilities have allowed machine learning and deep learning algorithms to analyze skin lesion images. Deep Convolutional Neural Networks (DCNNs) have achieved more encouraging results, yet faster systems for diagnosing fatal diseases are the need of the hour. This paper presents a survey of techniques for skin cancer detection from images. The paper aims to present a review of existing state-of-the-art and effective models for automatically detecting Melanoma from skin images. The result of classifications and segmentation from the skin lesion images will be processed better using the ensemble deep learning algorithm.

scholarly journals Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers

2021 ◽  
Vol 22 (21) ◽  
pp. 11919
Author(s):  
Abdullah Al Mamun ◽  
Raihanul Bari Tanvir ◽  
Masrur Sobhan ◽  
Kalai Mathee ◽  
Giri Narasimhan ◽  
...  
Keyword(s):  
Feature Selection ◽  
Target Genes ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Expression Profiles ◽  
Risk Groups ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Stable Set ◽  
Deep Learning Algorithm

Background: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. Method: To discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers. Results: Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers. Conclusion: The proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers.

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