Thyroid nodules classification and diagnosis in ultrasound images using fine‐tuning deep convolutional neural network

2019 ◽  
Vol 30 (1) ◽  
pp. 185-195 ◽  
Author(s):  
Olfa Moussa ◽  
Hajer Khachnaoui ◽  
Ramzi Guetari ◽  
Nawres Khlifa
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Thyroid Nodules ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Ultrasound Images

Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

2021 ◽  
pp. 1-10
Author(s):  
Gayatri Pattnaik ◽  
Vimal K. Shrivastava ◽  
K. Parvathi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Data Augmentation ◽  
State Of The Art ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Tomato Plants ◽  
Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

scholarly journals Skin Lesion Classification With Deep Convolutional Neural Network: Process Development and Validation

10.2196/18438 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e18438
Author(s):  
Arnab Ray ◽  
Aman Gupta ◽  
Amutha Al
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Process Development ◽  
Early Stage ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Network Process ◽  
Lesion Classification

Background Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. Objective We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. Methods We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. Results We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. Conclusions Based on our research, we conclude that deep learning algorithms are highly suitable for classifying skin cancer images.

scholarly journals Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

2018 ◽  
Vol 13 (12) ◽  
pp. 1895-1903 ◽  
Author(s):  
Michał Byra ◽  
Grzegorz Styczynski ◽  
Cezary Szmigielski ◽  
Piotr Kalinowski ◽  
Łukasz Michałowski ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Liver Steatosis ◽  
Deep Convolutional Neural Network ◽  
Ultrasound Images

Pre-Trained Convolutional Neural Networks for Breast Cancer Detection Using Ultrasound Images

2021 ◽  
Vol 21 (4) ◽  
pp. 1-17
Author(s):  
Mehedi Masud ◽  
M. Shamim Hossain ◽  
Hesham Alhumyani ◽  
Sultan S. Alshamrani ◽  
Omar Cheikhrouhou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Cancer Detection ◽  
Early Detection Of Cancer ◽  
Fine Tuning ◽  
Volunteer Computing ◽  
Ultrasound Images ◽  
Key Features

Volunteer computing based data processing is a new trend in healthcare applications. Researchers are now leveraging volunteer computing power to train deep learning networks consisting of billions of parameters. Breast cancer is the second most common cause of death in women among cancers. The early detection of cancer may diminish the death risk of patients. Since the diagnosis of breast cancer manually takes lengthy time and there is a scarcity of detection systems, development of an automatic diagnosis system is needed for early detection of cancer. Machine learning models are now widely used for cancer detection and prediction research for improving the successive therapy of patients. Considering this need, this study implements pre-trained convolutional neural network based models for detecting breast cancer using ultrasound images. In particular, we tuned the pre-trained models for extracting key features from ultrasound images and included a classifier on the top layer. We measured accuracy of seven popular state-of-the-art pre-trained models using different optimizers and hyper-parameters through fivefold cross validation. Moreover, we consider Grad-CAM and occlusion mapping techniques to examine how well the models extract key features from the ultrasound images to detect cancers. We observe that after fine tuning, DenseNet201 and ResNet50 show 100% accuracy with Adam and RMSprop optimizers. VGG16 shows 100% accuracy using the Stochastic Gradient Descent optimizer. We also develop a custom convolutional neural network model with a smaller number of layers compared to large layers in the pre-trained models. The model also shows 100% accuracy using the Adam optimizer in classifying healthy and breast cancer patients. It is our belief that the model will assist healthcare experts with improved and faster patient screening and pave a way to further breast cancer research.

scholarly journals Using the Deep Convolutional Neural Network to Evaluate Thyroid Nodules With Atypia of Undetermined Significance/follicular Lesion of Undetermined Significance Cytology: Multicenter Study

2021 ◽  
Author(s):  
Inyoung Youn ◽  
Eunjung Lee ◽  
Jung Hyun Yoon ◽  
Hye Sun Lee ◽  
Mi-Ri Kwon ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Diagnostic Performance ◽  
Thyroid Nodules ◽  
Interobserver Variability ◽  
Needle Aspiration ◽  
Deep Convolutional Neural Network ◽  
Fine Needle ◽  
Experience Levels ◽  
Undetermined Significance

Abstract To compare the diagnostic performances of physicians and a deep convolutional neural network (CNN) predicting malignancy with ultrasonography images of thyroid nodules with atypia of undetermined significance (AUS)/follicular lesion of undetermined significance (FLUS) results on fine-needle aspiration (FNA). This study included 202 patients with 202 nodules ≥ 1cm AUS/FLUS on FNA, and underwent surgery in one of 3 different institutions. Diagnostic performances were compared between 8 physicians (4 radiologists, 4 endocrinologists) with varying experience levels and CNN, and AUS/FLUS subgroups were analyzed. Interobserver variability was assessed among the 8 physicians. Of the 202 nodules, 158 were AUS, and 44 were FLUS; 86 were benign, and 116 were malignant. The area under the curves (AUCs) of the 8 physicians and CNN were 0.680-0.722 and 0.666, without significant differences (P > 0.05). In the subgroup analysis, the AUCs for the 8 physicians and CNN were 0.657–0.768 and 0.652 for AUS, 0.469-0.674 and 0.622 for FLUS. Interobserver agreements were moderate (k=0.543), substantial (k=0.652), and moderate (k=0.455) among the 8 physicians, 4 radiologists, and 4 endocrinologists. For thyroid nodules with AUS/FLUS cytology, the diagnostic performance of CNN to differentiate malignancy with US images was comparable to that of physicians with variable experience levels.

Classification of Deep Convolutional Neural Network in Thyroid Ultrasound Images

2020 ◽  
Vol 10 (8) ◽  
pp. 1943-1948
Author(s):  
Ran Hui ◽  
Jiaxing Chen ◽  
Yu Liu ◽  
Lin Shi ◽  
Chao Fu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
System Analysis ◽  
Ultrasound Image ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
Ultrasound Images ◽  
Thyroid Ultrasound ◽  
Neural Network Learning ◽  
Neural Network Theory

Objective: To explore the application of deep convolutional neural network theory in thyroid ultrasound image system analysis and eigenvalue extraction to help medically predict the patient’s condition. Methods: The thyroid color ultrasound image dataset of our hospital was selected as the training and test samples. The comparison experiment was designed in the deep convolutional neural network learning framework to test the feasibility of the method. Results: Image information classification based on deep neural network algorithm can predict thyroid nodule lesions well, and has good accuracy in the classification test of benign and malignant nodules. Conclusion: The clinical application of deep learning method and thyroid ultrasound image feature value extraction and system analysis can improve the accuracy of clinical thyroid benign and malignant classification.

Sign in / Sign up

Export Citation Format

Share Document