scholarly journals Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization

2017 ◽  
Author(s):  
Frederick Tung ◽  
Srikanth Muralidharan ◽  
Greg Mori
Keyword(s):  
Fine Tuning ◽  
Bayesian Optimization ◽  
Convolutional Network

scholarly journals Characterizing Malignant Melanoma Clinically Resembling Seborrheic Keratosis Using Deep Knowledge Transfer

Cancers ◽  
2021 ◽  
Vol 13 (24) ◽  
pp. 6300
Author(s):  
Panagiota Spyridonos ◽  
George Gaitanis ◽  
Aristidis Likas ◽  
Ioannis Bassukas
Keyword(s):  
Public Access ◽  
Classification Model ◽  
Bayesian Optimization ◽  
Support Vector ◽  
Seborrheic Keratosis ◽  
Convolutional Network ◽  
Data Set ◽  
Computer Based ◽  
Hyperparameter Selection

Malignant melanomas resembling seborrheic keratosis (SK-like MMs) are atypical, challenging to diagnose melanoma cases that carry the risk of delayed diagnosis and inadequate treatment. On the other hand, SK may mimic melanoma, producing a ‘false positive’ with unnecessary lesion excisions. The present study proposes a computer-based approach using dermoscopy images for the characterization of SΚ-like MMs. Dermoscopic images were retrieved from the International Skin Imaging Collaboration archive. Exploiting image embeddings from pretrained convolutional network VGG16, we trained a support vector machine (SVM) classification model on a data set of 667 images. SVM optimal hyperparameter selection was carried out using the Bayesian optimization method. The classifier was tested on an independent data set of 311 images with atypical appearance: MMs had an absence of pigmented network and had an existence of milia-like cysts. SK lacked milia-like cysts and had a pigmented network. Atypical MMs were characterized with a sensitivity and specificity of 78.6% and 84.5%, respectively. The advent of deep learning in image recognition has attracted the interest of computer science towards improved skin lesion diagnosis. Open-source, public access archives of skin images empower further the implementation and validation of computer-based systems that might contribute significantly to complex clinical diagnostic problems such as the characterization of SK-like MMs.

Melanoma Skin Cancer Recognition and Classification Using Deep Hybrid Learning

2021 ◽  
Vol 11 (12) ◽  
pp. 3110-3116
Author(s):  
Jansi Rani Sella Veluswami ◽  
M. Ezhil Prasanth ◽  
K. Harini ◽  
U. Ajaykumar
Keyword(s):  
Skin Cancer ◽  
Hybrid Approach ◽  
Hybrid Learning ◽  
Histogram Equalization ◽  
Fine Tuning ◽  
Classification Model ◽  
Support Vector ◽  
Common Disease ◽  
Convolutional Network ◽  
Melanoma Skin

Melanoma skin cancer is a common disease that develops in the melanocytes that produces melanin. In this work, a deep hybrid learning model is engaged to distinguish the skin cancer and classify them. The dataset used contains two classes of skin cancer–benign and malignant. Since the dataset is imbalanced between the number of images in malignant lesions and benign lesions, augmentation technique is used to balance it. To improve the clarity of the images, the images are then enhanced using Contrast Limited Adaptive Histogram Equalization Technique (CLAHE) technique. To detect only the affected lesion area, the lesions are segmented using the neural network based ensemble model which is the result of combining the segmentation algorithms of Fully Convolutional Network (FCN), SegNet and U-Net which produces a binary image of the skin and the lesion, where the lesion is represented with white and the skin is represented by black. These binary images are further classified using different pre-trained models like Inception ResNet V2, Inception V3, Resnet 50, Densenet and CNN. Following that fine tuning of the best performing pre-trained model is carried out to improve the performance of classification. To further improve the performance of the classification model, a method of combining deep learning (DL) and machine learning (ML) is carried out. Using this hybrid approach, the feature extraction is done using DL models and the classification is performed by Support Vector Machine (SVM). This computer aided tool will assist doctors in diagnosing the disease faster than the traditional method. There is a significant improvement of nearly 4% increase in the performance of the proposed method is presented.

scholarly journals Relative depth estimation from single monocular images with deep convolutional network

2017 ◽  
Author(s):  
◽  
Alex Yang
Keyword(s):  
High Performance ◽  
Depth Estimation ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Relative Depth ◽  
Optimized Design ◽  
Convolutional Network ◽  
Real Time Processing ◽  
Deep Convolutional Network ◽  
Depth Inference

Depth estimation from single monocular images is a theoretical challenge in computer vision as well as a computational challenge in practice. This thesis addresses the problem of depth estimation from single monocular images using a deep convolutional neural fields framework; which consists of convolutional feature extraction, superpixel dimensionality reduction, and depth inference. Data were collected using a stereo vision camera, which generated depth maps though triangulation that are paired with visual images. The visual image (input) and computed depth map (desired output) are used to train the model, which has achieved 83 percent test accuracy at the standard 25 percent tolerance. The problem has been formulated as depth regression for superpixels and our technique is superior to existing state-of-the-art approaches based on its demonstrated its generalization ability, high prediction accuracy, and real-time processing capability. We utilize the VGG-16 deep convolutional network as feature extractor and conditional random fields depth inference. We have leveraged a multi-phase training protocol that includes transfer learning and network fine-tuning lead to high performance accuracy. Our framework has a robust modular nature with capability of replacing each component with different implementations for maximum extensibility. Additionally, our GPU-accelerated implementation of superpixel pooling has further facilitated this extensibility by allowing incorporation of feature tensors with exible shapes and has provided both space and time optimization. Based on our novel contributions and high-performance computing methodologies, the model achieves a minimal and optimized design. It is capable of operating at 30 fps; which is a critical step towards empowering real-world applications such as autonomous vehicle with passive relative depth perception using single camera vision-based obstacle avoidance, environment mapping, etc.

scholarly journals Global Variance as a Utility Function in Bayesian Optimization

2021 ◽  
Vol 3 (1) ◽  
pp. 3
Author(s):  
Roland Preuss ◽  
Udo von Toussaint
Keyword(s):  
Utility Function ◽  
Parameter Space ◽  
Region Of Interest ◽  
Optimization Procedure ◽  
Target Surface ◽  
Good Choice ◽  
Fine Tuning ◽  
Bayesian Optimization ◽  
Surrogate Surface ◽  
Tool Set

A Gaussian-process surrogate model based on already acquired data is employed to approximate an unknown target surface. In order to optimally locate the next function evaluations in parameter space a whole variety of utility functions are at one’s disposal. However, good choice of a specific utility or a certain combination of them prepares the fastest way to determine a best surrogate surface or its extremum for lowest amount of additional data possible. In this paper, we propose to consider the global (integrated) variance as an utility function, i.e., to integrate the variance of the surrogate over a finite volume in parameter space. It turns out that this utility not only complements the tool set for fine tuning investigations in a region of interest but expedites the optimization procedure in toto.

scholarly journals Transfer Learning Assisted Classification and Detection of Alzheimer’s Disease Stages Using 3D MRI Scans

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2645 ◽  
Author(s):  
Muazzam Maqsood ◽  
Faria Nazir ◽  
Umair Khan ◽  
Farhan Aadil ◽  
Habibullah Jamal ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Transfer Learning ◽  
Brain Magnetic Resonance Imaging ◽  
Fine Tuning ◽  
Brain Cells ◽  
Daily Routine ◽  
Convolutional Network ◽  
Segmented Images ◽  
Multi Class Classification

Alzheimer’s disease effects human brain cells and results in dementia. The gradual deterioration of the brain cells results in disability of performing daily routine tasks. The treatment for this disease is still not mature enough. However, its early diagnosis may allow restraining the spread of disease. For early detection of Alzheimer’s through brain Magnetic Resonance Imaging (MRI), an automated detection and classification system needs to be developed that can detect and classify the subject having dementia. These systems also need not only to classify dementia patients but to also identify the four progressing stages of dementia. The proposed system works on an efficient technique of utilizing transfer learning to classify the images by fine-tuning a pre-trained convolutional network, AlexNet. The architecture is trained and tested over the pre-processed segmented (Grey Matter, White Matter, and Cerebral Spinal Fluid) and un-segmented images for both binary and multi-class classification. The performance of the proposed system is evaluated over Open Access Series of Imaging Studies (OASIS) dataset. The algorithm showed promising results by giving the best overall accuracy of 92.85% for multi-class classification of un-segmented images.

scholarly journals IOU-Guided Siamese Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jianjun Bao ◽  
Haibo Wang ◽  
Chen Lv ◽  
Ke Luo ◽  
Xiaolin Shen
Keyword(s):  
Target Tracking ◽  
Data Augmentation ◽  
Target Selection ◽  
Fine Tuning ◽  
Target Area ◽  
Decision Strategy ◽  
Tracking Accuracy ◽  
Convolutional Network ◽  
Tuning Method ◽  
Siamese Network

Target tracking is currently a hot research topic in machine vision. The traditional target tracking algorithm based on the generative model selects target features manually, which has a simple structure and fast running speed, but it cannot meet the requirements of algorithm accuracy in complex scenes. Compared with traditional algorithms, due to the good performance, the tracking method based on full convolutional network has become one of the important methods of target tracking. However, the RPN-based Siamese network lacks positional reliability when predicting the target area. Aiming at the low tracking accuracy of the RPN-based Siamese network, this paper proposes an improved framework model named IoU-guided SiamRPN (IG-SiamRPN). In the proposed IG-SiamRPN, the IoU-guided branch is first constructed and sample pairs are generated through data augmentation. Then, the Jittered RoI is constructed to train the network to realize the direct prediction of the localization confidence of the candidate area. Subsequently, a target selection method based on predicted IoU scores is proposed, which uses predicted IoU scores instead of classification scores to optimize the target decision strategy of the Siamese network. Finally, an optimization-based fine-tuning method for the Siamese network frame is proposed, which solves the problem of location degradation and improves the performance of the algorithm. Compared with other state-of-the-art target tracking algorithms, experimental results on popular databases demonstrate that the proposed IG-SiamRPN can achieve better performance in both tracking accuracy and robustness.

scholarly journals Fault Diagnosis of Rolling Bearings Based on a Residual Dilated Pyramid Network and Full Convolutional Denoising Autoencoder

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5734 ◽  
Author(s):  
Hongmei Shi ◽  
Jingcheng Chen ◽  
Jin Si ◽  
Changchang Zheng
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Fine Tuning ◽  
Noise Robustness ◽  
Intelligent Fault Diagnosis ◽  
Denoising Autoencoder ◽  
Rolling Bearings ◽  
Convolutional Network ◽  
Time Frequency ◽  
Diagnosis Algorithm

Intelligent fault diagnosis algorithm for rolling bearings has received increasing attention. However, in actual industrial environments, most rolling bearings work under severe working conditions of variable speed and strong noise, which makes the performance of many intelligent fault diagnosis methods deteriorate sharply. In this regard, this paper proposes a new intelligent diagnosis algorithm for rolling bearing faults based on a residual dilated pyramid network and full convolutional denoising autoencoder (RDPN-FCDAE). First, a continuous wavelet transform (CWT) is used to convert original vibration signals into time-frequency images. Secondly, a deep two-stage RDPN-FCDAE model is constructed, which is divided into three parts: encoding network, decoding network and classification network. In order to obtain efficient expression of data denoising feature of encoding network, time-frequency images are first input into the encoding-decoding network for unsupervised pre-training. Then pre-trained coding network and classification network are combined into residual dilated pyramid full convolutional network (RDPFCN) for parameter fine-tuning and testing. The proposed method is applied to bearing vibration datasets of test rig with different speeds and noise modes. Compared with representative machine learning and deep learning method, the results show that the algorithm proposed is superior to other methods in diagnostic accuracy, noise robustness and feature segmentation ability.

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