Breast-Cancer Detection using Thermal Images with Marine-Predators-Algorithm Selected Features

Rajinikanth, V.; Kadry, Seifedine; Taniar, David; Damaševičius, Robertas; Rauf, Hafiz Tayyab

doi:10.1109/icbsii51839.2021.9445166

Cited by 44 publications

(20 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are numerous breast cancer classification processes that are mentioned on literature review, 13,[26][27][28][29][30][31][32][33][34][35][36] which methods have some limitations, like low detection rate of benign samples likened with high fraction, the accuracy of Benign is obviously categorize and diminish that accuracy of malignant, some processes do not obviously categorize malignant and benign display the accuracy of normal region, certain approaches display the accuracy of image. In this process it overcomes all these problems and provides more precision.…”

Section: Problem Statementmentioning

confidence: 99%

“…Rajinikanth et al, 32 have suggested the breast cancer detection by screening system using breast thermal imaging (BTI) with selected features of the marine predators algorithm. The implemented methodology is below; (i) image recording for several breast orientations, (ii) extraction of healthy/DCIS image patches, (iii) treatment of patches through image processing, (iv) feature extraction, (v) feature optimization through marine predators‐algorithm (MPA), and (vi) classification and authentication under two classes.…”

Section: Literature Surveymentioning

confidence: 99%

See 1 more Smart Citation

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Sakthivel¹,

Subasree

Malik

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

In this manuscript, a Wrapper based feature extraction framework based on AlexNet deep convolutional neural network (ADCNN) with gradient‐based optimizer (GBO) is proposed for early detection of breast cancer. In this input images are occupied as mini‐mammography image analysis society (MIAS) database. Then the images are pre‐processed to eliminate that noises using Markov random field (MRF) method. Image features are extracted by the process of Wrapper based feature extraction framework with ADCNN. Then, the weight parameters of ADCNN are optimizing through the aid of GBO. Then the mammogram images are characterized as normal or abnormal (malignant and benign) with SVM classifier. The simulation process is implemented on MATLAB platform. The proposed ADCNN‐SVM‐GBO attains higher accuracy 34.64%, 28.86%, 19.86%, 24.64%, 32.86%, higher Precision 28.07%, 18.96%, 16.86%, 25.86%, 26.86%, higher recall 27.86%, 32.54%, 27.86%, 23.95%, 19.97%, and the efficiency of the proposed method FE‐ADCNN‐GBO‐SVM is likened with the existing processes. Classification of mammograms depends features removal processes with support vector machine (FE‐LBP‐GLCM‐SVM), breast cancer classification with global discriminate features on mammographic images (FE‐GLCM‐ANN), enhancing breast cancer classification with (SMOTE) technique and pectoral muscle removal on mammographic images (FE‐SMOTE‐RF), application of artificial intelligence depends deep learning on breast cancer screening and imaging diagnosis (FE‐CNN‐CDCNN) respectively.

show abstract

Section: Problem Statementmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Sakthivel¹,

Subasree

Malik

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Using Equation ( 4), 𝜎 can be calculated. The signal variance 𝜎 can be calculated using Equation (5). Using 𝜎 and 𝜎𝜎 , the Bayes threshold has been calculated as in Equation ( 5).…”

Section: Image Enhancementmentioning

confidence: 99%

“…However, effectively diagnosing breast cancer at an early stage can increase the possibility of total recovery. Early detection of breast cancer using mammography [2] and other imaging modalities such as ultrasound [3], magnetic resonance imaging (MRI) [4], and thermal images [5] can help reduce the mortality rate and the probability of recurrence due to the early detection of benign and malignant breast cancer masses with the progress of mammogram imaging. However, expert radiologists are still missing a significant proportion of abnormalities in the early stage of cancer.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Zebari

Ibrahim²,

Zeebaree

et al. 2021

Applied Sciences

View full text Add to dashboard Cite

Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.

show abstract

“…In recent years, deep learning showed good results in image classification, especially in object detection by the use of the convolutional neural networks (CNNs) architectures which are the leading algorithms in BC classification, [7][8][9] brain tumor segmentation, 10,11 and in many other computer vision tasks. [12][13][14][15][16] Although CNN succeeded in classifying images, they still need more improvement to avoid the overfitting, under-fitting, and the use of the different preprocessing techniques that take the time and the resources in classifying histopathological and microscopic images.…”

Section: Introductionmentioning

confidence: 99%

Multiple instance learning for classifying histopathological images of the breast cancer using residual neural network

Abdelli

Saouli

Djemal

et al. 2022

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Digital histopathological images have complex textures and high variability. Thus, classifying histopathological images requires an accurate classification and recognition of the tissue components in these images. In this article, we propose a novel classification layer based on multiple instances learning (MIL). In regular convolutional neural network (CNN) a flatten or a global pooling layer is used before the fully connected layers. However, in our proposed layer, we consider each last feature map in the network as an instance that will be classified by the output layer. Then, an aggregation function will be applied to get the class of the image (bag). This mapping helps the model to classify each feature independently to catch the micro-objects of the complex tissue images. Also, our method succeeded in achieving high accuracy without the preprocessing of the images with color normalization, stain normalization, or any other techniques. Additionally, we trained our models in two different strategies. The first one is by combining the images from all the magnification factors, and the second is by training a model for each magnification factor. We show in this work that our model outperforms several previous works on breast cancer classification.

show abstract

Breast-Cancer Detection using Thermal Images with Marine-Predators-Algorithm Selected Features

Cited by 44 publications

References 21 publications

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

A Wrapper based feature extraction framework based on AlexNet deep convolutional neural network parameters optimized using gradient‐based optimizer for mammogram images

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Multiple instance learning for classifying histopathological images of the breast cancer using residual neural network

Contact Info

Product

Resources

About