Fusion-Based Semantic Segmentation Using Deep Learning Architecture in Case of Very Small Training Dataset

2021 ◽  
Author(s):  
Ganesh R. Padalkar ◽  
Madhuri B. Khambete
Keyword(s):  
Deep Learning ◽  
Real Life ◽  
Class Imbalance ◽  
Semantic Segmentation ◽  
Training Dataset ◽  
Processing Step ◽  
Effective Training ◽  
Unsupervised Algorithms ◽  
Learning Architectures ◽  
Small Dataset

Semantic segmentation is a pre-processing step in computer vision-based applications. It is the task of assigning a predefined class label to every pixel of an image. Several supervised and unsupervised algorithms are available to classify pixels of an image into predefined object classes. The algorithms, such as random forest and SVM are used to obtain the semantic segmentation. Recently, convolutional neural network (CNN)-based architectures have become popular for the tasks of object detection, object recognition, and segmentation. These deep architectures perform semantic segmentation with far better accuracy than the algorithms that were used earlier. CNN-based deep learning architectures require a large dataset for training. In real life, some of the applications may not have sufficient good quality samples for training of deep learning architectures e.g. medical applications. Such a requirement initiated a need to have a technique of effective training of deep learning architecture in case of a very small dataset. Class imbalance is another challenge in the process of training deep learning architecture. Due to class imbalance, the classifier overclassifies classes with large samples. In this paper, the challenge of training a deep learning architecture with a small dataset and class imbalance is addressed by novel fusion-based semantic segmentation technique which improves segmentation of minor and major classes.

scholarly journals Orchard Mapping with Deep Learning Semantic Segmentation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3813
Author(s):  
Athanasios Anagnostis ◽  
Aristotelis C. Tagarakis ◽  
Dimitrios Kateris ◽  
Vasileios Moysiadis ◽  
Claus Grøn Sørensen ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Automated Detection ◽  
Aerial Images ◽  
Training Dataset ◽  
Field Boundary ◽  
Different Seasons ◽  
Detection And Localization ◽  
Different Levels

This study aimed to propose an approach for orchard trees segmentation using aerial images based on a deep learning convolutional neural network variant, namely the U-net network. The purpose was the automated detection and localization of the canopy of orchard trees under various conditions (i.e., different seasons, different tree ages, different levels of weed coverage). The implemented dataset was composed of images from three different walnut orchards. The achieved variability of the dataset resulted in obtaining images that fell under seven different use cases. The best-trained model achieved 91%, 90%, and 87% accuracy for training, validation, and testing, respectively. The trained model was also tested on never-before-seen orthomosaic images or orchards based on two methods (oversampling and undersampling) in order to tackle issues with out-of-the-field boundary transparent pixels from the image. Even though the training dataset did not contain orthomosaic images, it achieved performance levels that reached up to 99%, demonstrating the robustness of the proposed approach.

scholarly journals Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251899
Author(s):  
Samir M. Badawy ◽  
Abd El-Naser A. Mohamed ◽  
Alaa A. Hefnawy ◽  
Hassan E. Zidan ◽  
Mohammed T. GadAllah ◽  
...  
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Deep Learning ◽  
Tissue Characterization ◽  
Semantic Segmentation ◽  
Batch Processing ◽  
Ultrasound Images ◽  
Processing Scheme ◽  
Processing Mode ◽  
Small Dataset

Computer aided diagnosis (CAD) of biomedical images assists physicians for a fast facilitated tissue characterization. A scheme based on combining fuzzy logic (FL) and deep learning (DL) for automatic semantic segmentation (SS) of tumors in breast ultrasound (BUS) images is proposed. The proposed scheme consists of two steps: the first is a FL based preprocessing, and the second is a Convolutional neural network (CNN) based SS. Eight well-known CNN based SS models have been utilized in the study. Studying the scheme was by a dataset of 400 cancerous BUS images and their corresponding 400 ground truth images. SS process has been applied in two modes: batch and one by one image processing. Three quantitative performance evaluation metrics have been utilized: global accuracy (GA), mean Jaccard Index (mean intersection over union (IoU)), and mean BF (Boundary F1) Score. In the batch processing mode: quantitative metrics’ average results over the eight utilized CNNs based SS models over the 400 cancerous BUS images were: 95.45% GA instead of 86.08% without applying fuzzy preprocessing step, 78.70% mean IoU instead of 49.61%, and 68.08% mean BF score instead of 42.63%. Moreover, the resulted segmented images could show tumors’ regions more accurate than with only CNN based SS. While, in one by one image processing mode: there has been no enhancement neither qualitatively nor quantitatively. So, only when a batch processing is needed, utilizing the proposed scheme may be helpful in enhancing automatic ss of tumors in BUS images. Otherwise applying the proposed approach on a one-by-one image mode will disrupt segmentation’s efficiency. The proposed batch processing scheme may be generalized for an enhanced CNN based SS of a targeted region of interest (ROI) in any batch of digital images. A modified small dataset is available: https://www.kaggle.com/mohammedtgadallah/mt-small-dataset (S1 Data).

scholarly journals Drone Image Segmentation Using Machine and Deep Learning for Mapping Raised Bog Vegetation Communities

2020 ◽  
Vol 12 (16) ◽  
pp. 2602 ◽  
Author(s):  
Saheba Bhatnagar ◽  
Laurence Gill ◽  
Bidisha Ghosh
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Computation Time ◽  
Semantic Segmentation ◽  
Training Dataset ◽  
Atmospheric Conditions ◽  
Raised Bog ◽  
Vegetation Communities ◽  
Lower Accuracy ◽  
Cost Efficient

The application of drones has recently revolutionised the mapping of wetlands due to their high spatial resolution and the flexibility in capturing images. In this study, the drone imagery was used to map key vegetation communities in an Irish wetland, Clara Bog, for the spring season. The mapping, carried out through image segmentation or semantic segmentation, was performed using machine learning (ML) and deep learning (DL) algorithms. With the aim of identifying the most appropriate, cost-efficient, and accurate segmentation method, multiple ML classifiers and DL models were compared. Random forest (RF) was identified as the best pixel-based ML classifier, which provided good accuracy (≈85%) when used in conjunction graph cut algorithm for image segmentation. Amongst the DL networks, a convolutional neural network (CNN) architecture in a transfer learning framework was utilised. A combination of ResNet50 and SegNet architecture gave the best semantic segmentation results (≈90%). The high accuracy of DL networks was accompanied with significantly larger labelled training dataset, computation time and hardware requirements compared to ML classifiers with slightly lower accuracy. For specific applications such as wetland mapping where networks are required to be trained for each different site, topography, season, and other atmospheric conditions, ML classifiers proved to be a more pragmatic choice.

Deep learning architectures for semantic segmentation and automatic estimation of severity of foliar symptoms caused by diseases or pests

2021 ◽  
Vol 210 ◽  
pp. 129-142
Author(s):  
Juliano P. Gonçalves ◽  
Francisco A.C. Pinto ◽  
Daniel M. Queiroz ◽  
Flora M.M. Villar ◽  
Jayme G.A. Barbedo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Semantic Segmentation ◽  
Foliar Symptoms ◽  
Automatic Estimation ◽  
Learning Architectures

scholarly journals Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

PLoS ONE ◽  
2020 ◽  
Vol 15 (10) ◽  
pp. e0230605
Author(s):  
Cefa Karabağ ◽  
Martin L. Jones ◽  
Christopher J. Peddie ◽  
Anne E. Weston ◽  
Lucy M. Collinson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Hela Cells ◽  
Semantic Segmentation ◽  
Traditional Algorithm ◽  
Learning Architectures

scholarly journals Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints

2021 ◽  
Vol 13 (13) ◽  
pp. 2578
Author(s):  
Samir Touzani ◽  
Jessica Granderson
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Open Data ◽  
Semantic Segmentation ◽  
Training Data ◽  
Training Dataset ◽  
Automated Extraction ◽  
Remote Sensing Images ◽  
Benchmark Datasets ◽  
Building Footprint

Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.

scholarly journals Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery

2021 ◽  
Vol 11 (19) ◽  
pp. 9180
Author(s):  
Siangruei Wu ◽  
Yihong Wu ◽  
Haoyun Chang ◽  
Florence T. Su ◽  
Hengchun Liao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Sampling Method ◽  
State Of The Art ◽  
Pituitary Tumors ◽  
Semantic Segmentation ◽  
Brain Lesions ◽  
Training Dataset ◽  
Training Set ◽  
Segmentation Algorithms ◽  
Method Model

Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwannoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes.

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