Deep learning architecture using transfer learning for classification of skin lesions

2021 ◽  
Author(s):  
S. P. Godlin Jasil ◽  
V. Ulagamuthalvi
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Skin Lesions

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.

Fusion of Visual and Anamnestic Data for the Classification of Skin Lesions with Deep Learning

2019 ◽  
pp. 211-219
Author(s):  
Simone Bonechi ◽  
Monica Bianchini ◽  
Pietro Bongini ◽  
Giorgio Ciano ◽  
Giorgia Giacomini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Skin Lesions

Comparing the capabilities of transfer learning models to detect skin lesion in humans

2020 ◽  
Vol 234 (10) ◽  
pp. 1083-1093 ◽  
Author(s):  
Aditi Singhal ◽  
Ramesht Shukla ◽  
Pavan Kumar Kankar ◽  
Saurabh Dubey ◽  
Sukhjeet Singh ◽  
...  
Keyword(s):  
Skin Lesion ◽  
Transfer Learning ◽  
Wiener Filter ◽  
Skin Lesions ◽  
Automated Classification ◽  
Convolutional Networks ◽  
Image Quantization ◽  
Malignant Skin ◽  
Effective Diagnosis

Effective diagnosis of skin tumours mainly relies on the analysis of the characteristics of the lesion. Automatic detection of malignant skin lesion has become a mandatory task to reduce the risk of human deaths and increase their survival. This article proposes a study of skin lesion classification using transfer learning approach. The transfer learning model uses four different state-of-the-art architectures, namely Inception v3, Residual Networks (ResNet 50), Dense Convolutional Networks (DenseNet 201) and Inception Residual Networks (Inception ResNet v2). These models are trained under the dataset comprising seven different classes of skin lesions. The skin lesion images are pre-processed using image quantization, grayscaling and the Wiener filter before final training step. These models are compared for performance evaluation on different metrics. The present study shows the efficacy of the methodology for automated classification of lesion images.

scholarly journals Transfer Learning with Convolutional Neural Networks for Diabetic Retinopathy Image Classification. A Review

2020 ◽  
Vol 10 (6) ◽  
pp. 2021 ◽  
Author(s):  
Ibrahem Kandel ◽  
Mauro Castelli
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Transfer Learning ◽  
Main Idea ◽  
Diabetic Patients ◽  
Domain Transfer ◽  
Domain Transfer Learning ◽  
Enormous Number ◽  
Deep Learning Model

Diabetic retinopathy (DR) is a dangerous eye condition that affects diabetic patients. Without early detection, it can affect the retina and may eventually cause permanent blindness. The early diagnosis of DR is crucial for its treatment. However, the diagnosis of DR is a very difficult process that requires an experienced ophthalmologist. A breakthrough in the field of artificial intelligence called deep learning can help in giving the ophthalmologist a second opinion regarding the classification of the DR by using an autonomous classifier. To accurately train a deep learning model to classify DR, an enormous number of images is required, and this is an important limitation in the DR domain. Transfer learning is a technique that can help in overcoming the scarcity of images. The main idea that is exploited by transfer learning is that a deep learning architecture, previously trained on non-medical images, can be fine-tuned to suit the DR dataset. This paper reviews research papers that focus on DR classification by using transfer learning to present the best existing methods to address this problem. This review can help future researchers to find out existing transfer learning methods to address the DR classification task and to show their differences in terms of performance.

scholarly journals Deep Learning Models for Classification of Red Blood Cells in Microscopy Images to Aid in Sickle Cell Anemia Diagnosis

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 427 ◽  
Author(s):  
Laith Alzubaidi ◽  
Mohammed A. Fadhel ◽  
Omran Al-Shamma ◽  
Jinglan Zhang ◽  
Ye Duan
Keyword(s):  
Deep Learning ◽  
Red Blood Cells ◽  
Transfer Learning ◽  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Blood Cells ◽  
Learning Models ◽  
Sickle Cells ◽  
Microscopy Images

Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death. The key function of red blood cells (erythrocytes) is to supply all the parts of the human body with oxygen. Red blood cells (RBCs) form a crescent or sickle shape when sickle cell anemia affects them. This abnormal shape makes it difficult for sickle cells to move through the bloodstream, hence decreasing the oxygen flow. The precise classification of RBCs is the first step toward accurate diagnosis, which aids in evaluating the danger level of sickle cell anemia. The manual classification methods of erythrocytes require immense time, and it is possible that errors may be made throughout the classification stage. Traditional computer-aided techniques, which have been employed for erythrocyte classification, are based on handcrafted features techniques, and their performance relies on the selected features. They also are very sensitive to different sizes, colors, and complex shapes. However, microscopy images of erythrocytes are very complex in shape with different sizes. To this end, this research proposes lightweight deep learning models that classify the erythrocytes into three classes: circular (normal), elongated (sickle cells), and other blood content. These models are different in the number of layers and learnable filters. The available datasets of red blood cells with sickle cell disease are very small for training deep learning models. Therefore, addressing the lack of training data is the main aim of this paper. To tackle this issue and optimize the performance, the transfer learning technique is utilized. Transfer learning does not significantly affect performance on medical image tasks when the source domain is completely different from the target domain. In some cases, it can degrade the performance. Hence, we have applied the same domain transfer learning, unlike other methods that used the ImageNet dataset for transfer learning. To minimize the overfitting effect, we have utilized several data augmentation techniques. Our model obtained state-of-the-art performance and outperformed the latest methods by achieving an accuracy of 99.54% with our model and 99.98% with our model plus a multiclass SVM classifier on the erythrocytesIDB dataset and 98.87% on the collected dataset.

A novel deep learning based framework for the detection and classification of breast cancer using transfer learning

2019 ◽  
Vol 125 ◽  
pp. 1-6 ◽  
Author(s):  
SanaUllah Khan ◽  
Naveed Islam ◽  
Zahoor Jan ◽  
Ikram Ud Din ◽  
Joel J. P. C Rodrigues
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Transfer Learning

scholarly journals Deep Learning Approaches to Automated Video Classification of Upper Limb Tension Test

Healthcare ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1579
Author(s):  
Wansuk Choi ◽  
Seoyoon Heo
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
High Performance ◽  
Model Fit ◽  
Fine Tuning ◽  
Learning Approaches ◽  
Video Classification ◽  
Video Clips ◽  
Performance Computing

The purpose of this study was to classify ULTT videos through transfer learning with pre-trained deep learning models and compare the performance of the models. We conducted transfer learning by combining a pre-trained convolution neural network (CNN) model into a Python-produced deep learning process. Videos were processed on YouTube and 103,116 frames converted from video clips were analyzed. In the modeling implementation, the process of importing the required modules, performing the necessary data preprocessing for training, defining the model, compiling, model creation, and model fit were applied in sequence. Comparative models were Xception, InceptionV3, DenseNet201, NASNetMobile, DenseNet121, VGG16, VGG19, and ResNet101, and fine tuning was performed. They were trained in a high-performance computing environment, and validation and loss were measured as comparative indicators of performance. Relatively low validation loss and high validation accuracy were obtained from Xception, InceptionV3, and DenseNet201 models, which is evaluated as an excellent model compared with other models. On the other hand, from VGG16, VGG19, and ResNet101, relatively high validation loss and low validation accuracy were obtained compared with other models. There was a narrow range of difference between the validation accuracy and the validation loss of the Xception, InceptionV3, and DensNet201 models. This study suggests that training applied with transfer learning can classify ULTT videos, and that there is a difference in performance between models.

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