GDN: A Stacking Network Used for Skin Cancer Diagnosis
- URL: http://arxiv.org/abs/2312.02437v1
- Date: Tue, 5 Dec 2023 02:33:55 GMT
- Title: GDN: A Stacking Network Used for Skin Cancer Diagnosis
- Authors: Jingmin Wei, Haoyang Shen, Ziyi Wang, Ziqian Zhang
- Abstract summary: This paper presents GoogLe-Dense Network (GDN), which is an image-classification model to identify two types of skin cancer, Basal Cell Carcinoma, and Melanoma.
GDN consists of two sequential levels in its structure. The first level performs basic classification tasks accomplished by GoogLeNet and DenseNet, which are trained in parallel to enhance efficiency.
We compare our method with four baseline networks including ResNet, VGGNet, DenseNet, and GoogLeNet on the dataset, in which GoogLeNet and DenseNet significantly outperform ResNet and VGGNet
- Score: 3.3091074760783554
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Skin cancer, the primary type of cancer that can be identified by visual
recognition, requires an automatic identification system that can accurately
classify different types of lesions. This paper presents GoogLe-Dense Network
(GDN), which is an image-classification model to identify two types of skin
cancer, Basal Cell Carcinoma, and Melanoma. GDN uses stacking of different
networks to enhance the model performance. Specifically, GDN consists of two
sequential levels in its structure. The first level performs basic
classification tasks accomplished by GoogLeNet and DenseNet, which are trained
in parallel to enhance efficiency. To avoid low accuracy and long training
time, the second level takes the output of the GoogLeNet and DenseNet as the
input for a logistic regression model. We compare our method with four baseline
networks including ResNet, VGGNet, DenseNet, and GoogLeNet on the dataset, in
which GoogLeNet and DenseNet significantly outperform ResNet and VGGNet. In the
second level, different stacking methods such as perceptron, logistic
regression, SVM, decision trees and K-neighbor are studied in which Logistic
Regression shows the best prediction result among all. The results prove that
GDN, compared to a single network structure, has higher accuracy in optimizing
skin cancer detection.
Related papers
- Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI [58.809276442508256]
We propose a hybrid network via the combination of convolution neural network (CNN) and transformer layers.
The experimental results on private and public DCE-MRI datasets demonstrate that the proposed hybrid network superior performance than the state-of-the-art methods.
arXiv Detail & Related papers (2024-08-11T15:46:00Z) - Graph Neural Networks Provably Benefit from Structural Information: A
Feature Learning Perspective [53.999128831324576]
Graph neural networks (GNNs) have pioneered advancements in graph representation learning.
This study investigates the role of graph convolution within the context of feature learning theory.
arXiv Detail & Related papers (2023-06-24T10:21:11Z) - Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular
Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation [1.3701366534590498]
Ocular Toxoplasmosis (OT) is a common eye infection caused by T. gondii that can cause vision problems.
This research seeks to provide a guide for future researchers looking to utilise DL techniques and develop a cheap, automated, easy-to-use, and accurate diagnostic method.
arXiv Detail & Related papers (2023-05-18T13:42:15Z) - Application of Transfer Learning and Ensemble Learning in Image-level
Classification for Breast Histopathology [9.037868656840736]
In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features.
This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions.
Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of $98.90%$.
arXiv Detail & Related papers (2022-04-18T13:31:53Z) - Stain Normalized Breast Histopathology Image Recognition using
Convolutional Neural Networks for Cancer Detection [9.826027427965354]
Recent advances have shown that the convolutional Neural Network (CNN) architectures can be used to design a Computer Aided Diagnostic (CAD) System for breast cancer detection.
We consider some contemporary CNN models for binary classification of breast histopathology images.
We have validated the trained CNN networks on a publicly available BreaKHis dataset, for 200x and 400x magnified histopathology images.
arXiv Detail & Related papers (2022-01-04T03:09:40Z) - Implementing a foveal-pit inspired filter in a Spiking Convolutional
Neural Network: a preliminary study [0.0]
We have presented a Spiking Convolutional Neural Network (SCNN) that incorporates retinal foveal-pit inspired Difference of Gaussian filters and rank-order encoding.
The model is trained using a variant of the backpropagation algorithm adapted to work with spiking neurons, as implemented in the Nengo library.
The network has achieved up to 90% accuracy, where loss is calculated using the cross-entropy function.
arXiv Detail & Related papers (2021-05-29T15:28:30Z) - An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation [53.425900196763756]
We propose a segmentation refinement method based on uncertainty analysis and graph convolutional networks.
We employ the uncertainty levels of the convolutional network in a particular input volume to formulate a semi-supervised graph learning problem.
We show that our method outperforms the state-of-the-art CRF refinement method by improving the dice score by 1% for the pancreas and 2% for spleen.
arXiv Detail & Related papers (2020-12-06T18:55:07Z) - C-Net: A Reliable Convolutional Neural Network for Biomedical Image
Classification [6.85316573653194]
We propose a novel convolutional neural network (CNN) architecture composed of a Concatenation of multiple Networks, called C-Net, to classify biomedical images.
The C-Net model outperforms all other models on the individual metrics for both datasets and achieves zero misclassification.
arXiv Detail & Related papers (2020-10-30T20:03:20Z) - DONet: Dual Objective Networks for Skin Lesion Segmentation [77.9806410198298]
We propose a simple yet effective framework, named Dual Objective Networks (DONet), to improve the skin lesion segmentation.
Our DONet adopts two symmetric decoders to produce different predictions for approaching different objectives.
To address the challenge of large variety of lesion scales and shapes in dermoscopic images, we additionally propose a recurrent context encoding module (RCEM)
arXiv Detail & Related papers (2020-08-19T06:02:46Z) - Pairwise Relation Learning for Semi-supervised Gland Segmentation [90.45303394358493]
We propose a pairwise relation-based semi-supervised (PRS2) model for gland segmentation on histology images.
This model consists of a segmentation network (S-Net) and a pairwise relation network (PR-Net)
We evaluate our model against five recent methods on the GlaS dataset and three recent methods on the CRAG dataset.
arXiv Detail & Related papers (2020-08-06T15:02:38Z) - Binarized Graph Neural Network [65.20589262811677]
We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters.
Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches.
Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
arXiv Detail & Related papers (2020-04-19T09:43:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.