Automatic detection metastasis in breast histopathological images based on ensemble learning and color adjustment

Luz, Daniel S.; Lima, Thiago; Silva, Romuere; Magalhães, Deborah; Araújo, Flávio H. D.

doi:10.1016/j.bspc.2022.103564

Cited by 21 publications

(12 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As they were trained on a general purpose database (Imagenet), their first layers formed by convolutional filters learn to extract generic features from images. This technique is commonly applied to medical imaging problems, as we can see in the literature [ 32 , 50 , 51 ]. Table 2 presents the pre-trained networks, layers used to extract the characteristics and quantity of characteristics of each network.…”

Section: Proposed Methodsmentioning

confidence: 99%

Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Lima

Luz

Oseas

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

Lung cancer has the highest incidence in the world. The standard tests for its diagnostics are medical imaging exams, sputum cytology, and lung biopsy. Computed Tomography (CT) of the chest plays an essential role in the early detection of nodules since it can allow for more treatment options and increases patient survival. However, the analysis of these exams is a tiring and error-prone process. Thus, computational methods can help the specialist in this analysis. This work addresses the classification of pulmonary nodules as benign or malignant on CT images. Our approach uses the pre-trained VGG16, VGG19, Inception, Resnet50, and Xception, to extract features from each 2D slice of the 3D nodules. Then, we use Principal Component Analysis to reduce the dimensionality of the feature vectors and make them all the same length. Then, we use Bag of Features (BoF) to combine the feature vectors of the different 2D slices and generate only one signature representing the 3D nodule. The classification step uses Random Forest. We evaluated the proposed method with 1,405 segmented nodules from the LIDC-IDRI database and obtained an accuracy of 95.34%, F1-Score of 91.73, kappa of 0.88, sensitivity of 90.53%, specificity of 97.26% and AUC of 0.99. The main conclusion was that the combination by BoF of features extracted from 2D slices using pre-trained architectures produced better results than training 2D and 3D CNNs in the nodules. In addition, the use of BoF also makes the creation of the nodule signature independent of the number of slices.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Lima

Luz

Oseas

et al. 2023

Multimed Tools Appl

View full text Add to dashboard Cite

show abstract

“…The CapsNet has been used for classification and detection in other areas, as in. [28][29][30][31][32] Hinton et al 33 proposed the capsule theory to solve some of CNN's disadvantages in 2011. The motivation for this study is the robust performance of the Caps-Net architecture on small, large, complex, and unbalanced data.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, a new model of artificial neural networks, Capsule Neural Network (CapsNet), has been used in many classification problems. The CapsNet has been used for classification and detection in other areas, as in 28–32 . Hinton et al 33 proposed the capsule theory to solve some of CNN's disadvantages in 2011.…”

Section: Introductionmentioning

confidence: 99%

Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset

Aydin Atasoy,

Faris Abdulla Al Rahhawi

2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

The automatic detection of bone marrow (BM) cell diseases plays a vital role in the medical field; it helps to make diagnoses more precise and effective, which leads to early detection and can significantly improve patient outcomes and increase the chances of successful intervention. This study proposed a fully automated intelligent system for BM classification by developing and enhancing Capsule Neural Network (CapsNet) architecture. Although CapsNet has demonstrated success in many classification tasks, it still has some limitations and challenges associated with using Convolutional Neural Networks (CNNs), which suffer from information loss during the pooling and discarding of detailed spatial information, resulting in the loss of fine‐grained features. Additionally, CNNs must help capture hierarchical feature relationships and often learn them implicitly by stacking convolutional layers. In contrast, CapsNets are designed to capture hierarchical features through dynamic routing and relationships between capsules, resulting in a more explicit representation of spatial hierarchy. CapsNets manage transformations and offer equivariance, preserving spatial information through capsule routing mechanisms. Further, to improve how features are represented, pre‐trained models such as Residual Capsule Network (RES‐CapsNet), Visual Geometry Group Capsule Network (VGG‐CapsNet), and Google Network (Inception V3) (GN‐CapsNet) have been used. This helps the network obtain the low‐ and mid‐level features and information it has previously learned so that subsequent capsule layers receive better initial information. Additionally, the Synthetic Minority Over‐Sampling Technique (SMOTE) was implemented to mitigate class imbalance. It generates synthetic samples in feature space by over‐sampling the minority class, leading to improving model performance in accurately classifying rare instances. Fine‐tuning the hyperparameters and implementing these improvements resulted in remarkable accuracy rates on a large BM dataset, with reduced training time and trainable parameters. CapsNet achieved 96.99%, VGG‐CapsNet achieved 98.95%, RES‐CapsNet achieved 99.24%, and the GN‐CapsNet model demonstrated superior accuracy at 99.45%. GN‐Caps Net was the best because it requires a small number of epochs and has an effective deep inception architecture that efficiently extracts features at different scales to form a robust representation of the input. Our proposed models were compared with existing state‐of‐the‐art models using the BM dataset; the results showed that our models outperformed the existing approaches and demonstrated excellent performance. Further, this automated system can analyze large amounts of data and complex cells in images of the BM dataset. Thus, it gives healthcare professionals a detailed understanding of different diseases, which may take time to achieve manually.

show abstract

“…We first design a lightweight CNN model with fewer parameters to perform the multiclass skin lesion classification by learning it from scratch. Further, inspired by the success of EL in medical image analysis tasks (Chen et al, 2021; Luz et al, 2022; Paul et al, 2021), we ensemble two different types of pre‐trained CNN models with the proposed lightweight model to further enhance the classification performance. The performance of

{normalM}^{2} CE

is shown to be improved compared to the single CNN models.…”

Section: Introductionmentioning

confidence: 99%

“…it from scratch. Further, inspired by the success of EL in medical image analysis tasks (Chen et al, 2021;Luz et al, 2022;Paul et al, 2021), we ensemble two different types of pre-trained CNN models with the proposed lightweight model to further enhance the classification performance.…”

mentioning

confidence: 99%

M²CE: Multi‐convolutional neural network ensemble approach for improved multiclass classification of skin lesion

Gajera,

Nayak,

Zaveri

2023

Expert Systems

View full text Add to dashboard Cite

Due to inter‐class homogeneity and intra‐class variability, the classification of skin lesions in dermoscopy images has remained difficult. Although deep convolutional neural networks (DCNNs) have achieved satisfactory performance for binary skin cancer classification, multiclass skin lesion classification is still an open problem due to the limited training samples and class imbalance issues. To tackle these issues, in this article, we propose a multi‐CNN ensemble approach dubbed for multiclass skin lesion classification. The includes three individual CNN models, each helping in extracting different high‐level features from skin images and thereby yielding different prediction results. First, we design a lightweight CNN model to extract prominent features and train it from scratch, which primarily aims at avoiding the data scarcity problem. Then, we ensemble two different pre‐trained CNN models with the lightweight model to improve the performance and generalization capability. The proposed ensemble approach can effectively fuse the predictions of each individual CNN model using the averaging method. The approach is validated using a benchmark data set, HAM10000, which contains skin lesion images of seven different classes. The results demonstrate that the outperforms base CNN models and state‐of‐the‐art approaches without using any external data.

show abstract

Automatic detection metastasis in breast histopathological images based on ensemble learning and color adjustment

Cited by 21 publications

References 37 publications

Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset

M²CE: Multi‐convolutional neural network ensemble approach for improved multiclass classification of skin lesion

Contact Info

Product

Resources

About

Automatic detection metastasis in breast histopathological images based on ensemble learning and color adjustment

Cited by 21 publications

References 37 publications

Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Automatic classification of pulmonary nodules in computed tomography images using pre-trained networks and bag of features

Examining the classification performance of pre‐trained capsule networks on imbalanced bone marrow cell dataset

M2CE: Multi‐convolutional neural network ensemble approach for improved multiclass classification of skin lesion

Contact Info

Product

Resources

About

M²CE: Multi‐convolutional neural network ensemble approach for improved multiclass classification of skin lesion