Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models

Jeon, Ho-Kun; Kim, Seungryong; Jonathan, Edwin; Yang, Chan-Su

doi:10.3390/electronics9020311

Cited by 33 publications

(24 citation statements)

References 21 publications

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“…Transfer learning is a commonly utilized technique when developing medical imaging models due to a lack of training data. One of the first ideas to use transfer learning was to adopt pretrained models of the ImageNet dataset instead of training from scratch [ 9 , 10 , 24 ]. Nevertheless, there is a significant difference between the characteristics of medical images and the natural image of datasets, such as ImageNet [ 7 ] (see Figure 1 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…This is commonly found in medical imaging due to the difficulty of collecting medical image datasets. The ILSVRC-2012 competition of ImageNet [ 7 ] is the most well-known pretraining dataset and has been extensively utilized to improve the performance of image processing tasks such as segmentation, detection, and classification [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

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Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data

Alzubaidi

Al-Amidie

Al-Asadi

et al. 2021

Cancers

183

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Deep learning requires a large amount of data to perform well. However, the field of medical image analysis suffers from a lack of sufficient data for training deep learning models. Moreover, medical images require manual labeling, usually provided by human annotators coming from various backgrounds. More importantly, the annotation process is time-consuming, expensive, and prone to errors. Transfer learning was introduced to reduce the need for the annotation process by transferring the deep learning models with knowledge from a previous task and then by fine-tuning them on a relatively small dataset of the current task. Most of the methods of medical image classification employ transfer learning from pretrained models, e.g., ImageNet, which has been proven to be ineffective. This is due to the mismatch in learned features between the natural image, e.g., ImageNet, and medical images. Additionally, it results in the utilization of deeply elaborated models. In this paper, we propose a novel transfer learning approach to overcome the previous drawbacks by means of training the deep learning model on large unlabeled medical image datasets and by next transferring the knowledge to train the deep learning model on the small amount of labeled medical images. Additionally, we propose a new deep convolutional neural network (DCNN) model that combines recent advancements in the field. We conducted several experiments on two challenging medical imaging scenarios dealing with skin and breast cancer classification tasks. According to the reported results, it has been empirically proven that the proposed approach can significantly improve the performance of both classification scenarios. In terms of skin cancer, the proposed model achieved an F1-score value of 89.09% when trained from scratch and 98.53% with the proposed approach. Secondly, it achieved an accuracy value of 85.29% and 97.51%, respectively, when trained from scratch and using the proposed approach in the case of the breast cancer scenario. Finally, we concluded that our method can possibly be applied to many medical imaging problems in which a substantial amount of unlabeled image data is available and the labeled image data is limited. Moreover, it can be utilized to improve the performance of medical imaging tasks in the same domain. To do so, we used the pretrained skin cancer model to train on feet skin to classify them into two classes—either normal or abnormal (diabetic foot ulcer (DFU)). It achieved an F1-score value of 86.0% when trained from scratch, 96.25% using transfer learning, and 99.25% using double-transfer learning.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data

Alzubaidi

Al-Amidie

Al-Asadi

et al. 2021

Cancers

183

View full text Add to dashboard Cite

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“…Among deep learning methods, a convolution neural network (CNN) has been widely used in image classification [12] and developed into various models [13][14][15]. CNN applied various fields such as sea fog recognition [16], traffic signal recognition [17] and face recognition [18].…”

Section: Introductionmentioning

confidence: 99%

“…This paper introduces the ship type prediction method from modified ship images and ship's size information with CNN [12][13][14][15][16][17][18][19][20][21][22][23][24] and K-Nearest Neighbor models [31], respectively. OpenSARship, one of the SARship datasets [25], is used in this experiment.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of Ship Type Classification from a Combination of CNN and KNN

Jeon

Yang

2021

Electronics

Self Cite

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Ship type classification of synthetic aperture radar imagery with convolution neural network (CNN) has been faced with insufficient labeled datasets, unoptimized and noised polarization images that can deteriorate a classification performance. Meanwhile, numerous labeled text information for ships, such as length and breadth, can be easily obtained from various sources and can be utilized in a classification with k-nearest neighbor (KNN). This study proposes a method to improve the efficiency of ship type classification from Sentinel-1 dual-polarization data with 10 m pixel spacing using both CNN and KNN models. In the first stage, Sentinel-1 intensity images centered on ship positions were used in a rectangular shape to apply an image processing procedure such as head-up, padding and image augmentation. The process increased the accuracy by 33.0% and 31.7% for VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization compared to the CNN-based classification with original ship images, respectively. In the second step, a combined method of CNN and KNN was compared with a CNN-alone case. The f1-score of CNN alone was up to 85.0%, whereas the combination method showed up to 94.3%, which was a 9.3% increase. In the future, more details on an optimization method will be investigated through field experiments of ship classification.

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“…Simply, the model is trained on a large amount of labeled data, such as (ImageNet). In the next step, the model is fine-tuned for training on small labeled data [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Transfer Learning Approach for Lumbar Spine Disc State Classification

Al-Kubaisi

Khamiss

2021

Electronics

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Recently, deep learning algorithms have become one of the most popular methods and forms of algorithms used in the medical imaging analysis process. Deep learning tools provide accuracy and speed in the process of diagnosing and classifying lumbar spine problems. Disk herniation and spinal stenosis are two of the most common lower back diseases. The process of diagnosing pain in the lower back can be considered costly in terms of time and available expertise. In this paper, we used multiple approaches to overcome the problem of lack of training data in disc state classification and to enhance the performance of disc state classification tasks. To achieve this goal, transfer learning from different datasets and a proposed region of interest (ROI) technique were implemented. It has been demonstrated that using transfer learning from the same domain as the target dataset may increase performance dramatically. Applying the ROI method improved the disc state classification results in VGG19 2%, ResNet50 16%, MobileNetV2 5%, and VGG16 2%. The results improved VGG16 4% and in VGG19 6%, compared with the transfer from ImageNet. Moreover, it has been stated that the closer the data to be classified is to the data that the system trained on, the better the achieved results will be.

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Sea Fog Identification from GOCI Images Using CNN Transfer Learning Models

Cited by 33 publications

References 21 publications

Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data

Novel Transfer Learning Approach for Medical Imaging with Limited Labeled Data

Enhancement of Ship Type Classification from a Combination of CNN and KNN

A Transfer Learning Approach for Lumbar Spine Disc State Classification

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