scholarly journals MRI Images, Brain Lesions and Deep Learning

2021 ◽  
Author(s):  
Darwin Castillo ◽  
Vasudevan Lakshminarayanan ◽  
María José Rodríguez-Álvarez
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Demyelinating Diseases ◽  
Clinical Environment ◽  
The Past ◽  
Demyelinating Lesions ◽  
Brain Image Analysis ◽  
Aided Diagnosis ◽  
Constant Growth

Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.

scholarly journals MR Images, Brain Lesions, and Deep Learning

2021 ◽  
Vol 11 (4) ◽  
pp. 1675
Author(s):  
Darwin Castillo ◽  
Vasudevan Lakshminarayanan ◽  
María José Rodríguez-Álvarez
Keyword(s):  
Deep Learning ◽  
Performance Metrics ◽  
Demyelinating Diseases ◽  
Computer Assisted ◽  
Dice Similarity Coefficient ◽  
Clinical Environment ◽  
Mr Images ◽  
Demyelinating Lesions ◽  
Brain Image Analysis

Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

scholarly journals Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chiaki Kuwada ◽  
Yoshiko Ariji ◽  
Yoshitaka Kise ◽  
Takuma Funakoshi ◽  
Motoki Fukuda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cleft Palate ◽  
Panoramic Radiography ◽  
False Positive Rate ◽  
Normal Subjects ◽  
Learning System ◽  
Panoramic Radiographs ◽  
Positive Rate ◽  
Aided Diagnosis

AbstractAlthough panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.

Deep Learning based Tomato’s Ripe and Unripe Classification System

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Ccd Camera ◽  
Agricultural Products ◽  
Training Data ◽  
Maturity Level ◽  
Agriculture Sector ◽  
The Past ◽  
State Of Art

Effective productivity estimates of fresh produced crops are very essential for efficient farming, commercial planning, and logistical support. In the past ten years, machine learning (ML) algorithms have been widely used for grading and classification of agricultural products in agriculture sector. However, the precise and accurate assessment of the maturity level of tomatoes using ML algorithms is still a quite challenging to achieve due to these algorithms being reliant on hand crafted features. Hence, in this paper we propose a deep learning based tomato maturity grading system that helps to increase the accuracy and adaptability of maturity grading tasks with less amount of training data. The performance of proposed system is assessed on the real tomato datasets collected from the open fields using Nikon D3500 CCD camera. The proposed approach achieved an average maturity classification accuracy of 99.8 % which seems to be quite promising in comparison to the other state of art methods.

Deep learning techniques for classification of brain MRI

2020 ◽  
Vol 19 (6) ◽  
pp. 571
Author(s):  
Arnab Kumar Maji ◽  
Imayanmosha Wahlang ◽  
Goutam Saha ◽  
Sugata Sanyal ◽  
Pallabi Sharma
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Learning Techniques

scholarly journals Interpretability of a Deep Learning Based Approach for the Classification of Skin Lesions into Main Anatomic Body Sites

Cancers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 6048
Author(s):  
Joanna Jaworek-Korjakowska ◽  
Andrzej Brodzicki ◽  
Bill Cassidy ◽  
Connah Kendrick ◽  
Moi Hoon Yap
Keyword(s):  
Deep Learning ◽  
Malignant Melanoma ◽  
Skin Lesions ◽  
The Body ◽  
Anatomic Site ◽  
The Past ◽  
Depth Analysis ◽  
The Face

Over the past few decades, different clinical diagnostic algorithms have been proposed to diagnose malignant melanoma in its early stages. Furthermore, the detection of skin moles driven by current deep learning based approaches yields impressive results in the classification of malignant melanoma. However, in all these approaches, the researchers do not take into account the origin of the skin lesion. It has been observed that the specific criteria for in situ and early invasive melanoma highly depend on the anatomic site of the body. To address this problem, we propose a deep learning architecture based framework to classify skin lesions into the three most important anatomic sites, including the face, trunk and extremities, and acral lesions. In this study, we take advantage of pretrained networks, including VGG19, ResNet50, Xception, DenseNet121, and EfficientNetB0, to calculate the features with an adjusted and densely connected classifier. Furthermore, we perform in depth analysis on database, architecture, and result regarding the effectiveness of the proposed framework. Experiments confirm the ability of the developed algorithms to classify skin lesions into the most important anatomical sites with 91.45% overall accuracy for the EfficientNetB0 architecture, which is a state-of-the-art result in this domain.

scholarly journals Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

2020 ◽  
Vol 10 (22) ◽  
pp. 8298
Author(s):  
Yuliana Jiménez-Gaona ◽  
María José Rodríguez-Álvarez ◽  
Vasudevan Lakshminarayanan
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Critical Review ◽  
Breast Tumor ◽  
Screening Tools ◽  
Breast Cancer Imaging ◽  
The Past ◽  
Computer Aided ◽  
Tumor Research ◽  
Aided Diagnosis

This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.

Deep learning techniques for classification of brain MRI

2020 ◽  
Vol 19 (6) ◽  
pp. 571
Author(s):  
Imayanmosha Wahlang ◽  
Pallabi Sharma ◽  
Sugata Sanyal ◽  
Goutam Saha ◽  
Arnab Kumar Maji
Keyword(s):  
Deep Learning ◽  
Brain Mri ◽  
Learning Techniques

scholarly journals A Deep Learning-Based Approach in Classification and Validation of Tomato Leaf Disease

2021 ◽  
Vol 38 (3) ◽  
pp. 699-709
Author(s):  
Shivali Amit Wagle ◽  
Harikrishnan R
Keyword(s):  
Deep Learning ◽  
Tomato Plant ◽  
Vital Role ◽  
Disease Classification ◽  
Learning Models ◽  
Plant Leaf ◽  
The Past ◽  
Leaf Disease ◽  
Number Of Classes

Deep learning models are playing a vital role in classification goals that can have propitious results. In the past few years, many models are being used for this purpose of plant disease classification. This work has assisted in the process of identification and classification of a plant leaf disease. In this paper, the Tomato plant leaf images are taken from the PlantVillage Database consisting of one healthy and eight disease classes. The disease classes are selected based on the occurrence of the disease in India. The deep learning models of AlexNet, VGG16, GoogLeNet, MobileNetv2, and SqueezeNet are used in this work for the classification of Tomato plant leaf as healthy or diseased and further which disease class it belongs to. The models used here are all the pre-trained models, so transfer learning is used to fit the total number of classes that need to be classified by the network model. VGG16 model outperformed giving 99.17% accuracy compared to AlexNet, GoogLeNet, MobileNetv2, and SqueezeNet. The work concludes with the model’s validation results on the set of images captured at Krishi Vigyan Kendra Narayangaon (KVKN), India.

scholarly journals A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

2021 ◽  
Vol 14 (3) ◽  
pp. 1231-1247
Author(s):  
Lokesh Singh ◽  
Rekh Ram Janghel ◽  
Satya Prakash Sahu
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Skin Lesions ◽  
Learning Framework ◽  
Melanoma Detection ◽  
Pigmented Skin Lesions ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Purpose:Less contrast between lesions and skin, blurriness, darkened lesion images, presence of bubbles, hairs are the artifactsmakes the issue challenging in timely and accurate diagnosis of melanoma. In addition, huge similarity amid nevus lesions and melanoma pose complexity in investigating the melanoma even for the expert dermatologists. Method: In this work, a computer-aided diagnosis for melanoma detection (CAD-MD) system is designed and evaluated for the early and accurate detection of melanoma using thepotentials of machine, and deep learning-based transfer learning for the classification of pigmented skin lesions. The designed CAD-MD comprises of preprocessing, segmentation, feature extraction and classification. Experiments are conducted on dermoscopic images of PH2 and ISIC 2016 publicly available datasets using machine learning and deep learning-based transfer leaning models in twofold: first, with actual images, second, with augmented images. Results:Optimal results are obtained on augmented lesion images using machine learning and deep learning models on PH2 and ISIC-16 dataset. The performance of the CAD-MD system is evaluated using accuracy, sensitivity, specificity, dice coefficient, and jacquard Index. Conclusion:Empirical results show that using the potentials of deep learning-based transfer learning model VGG-16 has significantly outperformed all employed models with an accuracy of 99.1% on the PH2 dataset.

scholarly journals Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Giang Son Tran ◽  
Thi Phuong Nghiem ◽  
Van Thi Nguyen ◽  
Chi Mai Luong ◽  
Jean-Christophe Burie
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Network Architecture ◽  
Lung Nodule ◽  
Pulmonary Nodules ◽  
Learning Method ◽  
Automatic Feature Extraction ◽  
Improving Accuracy ◽  
Aided Diagnosis

Early detection and classification of pulmonary nodules using computer-aided diagnosis (CAD) systems is useful in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning method to improve classification accuracy of pulmonary nodules in computed tomography (CT) scans. Our method uses a novel 15-layer 2D deep convolutional neural network architecture for automatic feature extraction and classification of pulmonary candidates as nodule or nonnodule. Focal loss function is then applied to the training process to boost classification accuracy of the model. We evaluated our method on the LIDC/IDRI dataset extracted by the LUNA16 challenge. The experiments showed that our deep learning method with focal loss is a high-quality classifier with an accuracy of 97.2%, sensitivity of 96.0%, and specificity of 97.3%.

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