An Infrared High classification Accuracy Hand-held Machine Learning based Breast-Cancer Detection System

Iqbal, Hafiz Talha; Majeed, Bilal; Khan, Uzair; Altaf, Muhammad Awais Bin

doi:10.1109/biocas.2019.8918687

Cited by 14 publications

(3 citation statements)

References 14 publications

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“…Another portable S60 thermal camera with a mobile phone was used in [48] to acquire images from 78 patients, 38 images of them for patients with breast cancer. The FLIR thermal imaging camera, with the best of four (BoF) features and the support vector machine (SVM) learning classifier, was used.…”

Section: Related Workmentioning

confidence: 99%

“…The study conducted by [52] used the new database in [48] to classify them using transfer learning with seven different deep learning pre-trained architectures: AlexNet, GoogLeNet, ResNet-50, ResNet-101, Inception V3, VGG-16 and VGG-19. Images were resized to a fixed size of 224×224 or 227×227 pixels, while the dataset was randomly split into 70% for training and 30% for validation.…”

Section: Related Workmentioning

confidence: 99%

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A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

et al. 2020

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

et al. 2020

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“…As a result, the X-rays can only penetrate a restricted area, resulting in more precise images. Iqbal et al (2019) to get the desired outcome, the grey level value of each pixel was reduced by 256 after the Database for Mastology Research (DMR) images were converted to grayscale. The following factors were considered before selecting the top five features: 32 features had the Gray Level Co-occurrence Matrix (GLCM) and Run Length Matrix (RLM) applied to them.…”

Section: Introductionmentioning

confidence: 99%

Radial Basis Function Networks and Contrast-Limited Adaptive Histogram Equalization Filter Based Early-Stage Breast Cancer Detection Techniques

Ponraj¹,

Canessane²

2023

Journal of Computer Science

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Breast cancer is one of the most common types of cancer that kills women. When cells become uncontrollably large, cancer develops. As a result, detecting and classifying breast cancer in its early stages is essential so that patients can take the appropriate precautions. On the other hand, mammography images have relatively low sensitivity and effectiveness in detecting breast cancer. Furthermore, MRI (Magnetic Resonance Imaging) has higher detection sensitivity for breast cancer than mammography. In this research, a novel Radial Basis Function Networks model (RBFN) with a Mayfly Optimization Algorithm (MAO) mechanism has Breast MRI scans aid in the early detection of breast cancer. Following the system's training on Magnetic Resonance Imaging (MRI) breast images, a unique Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter is developed for pre-processing noisy MRI image material. Backgrounds were removed before recovering breast cancer photos with a Contrast Limited Histogram Equalization (CLAHE) filter. Furthermore, the new study effort's performance is compared to earlier studies and this model is simulated using Python. The proposed model, RBFN-MAO, also outperforms previous models in terms of performance and precision with an accuracy of 97.54%. In comparison, it is 85.28, 80.95, 76.94, 85.39 and 90.32% for Convolution Neural Networks You Only Look Once (CNN-YOLO), Residual Networks (ResNet50), Diffusion Convolution Neural Networks (DCNN), Support Vector Machine (SVM) and Convolution Neural Network (CNN) models, respectively.

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Feature and Decision Fusion for Breast Cancer Detection

Yadav

Sharma

Pateriya

2022

Lecture Notes on Data Engineering and Communications Technologies

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An Infrared High classification Accuracy Hand-held Machine Learning based Breast-Cancer Detection System

Cited by 14 publications

References 14 publications

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

Radial Basis Function Networks and Contrast-Limited Adaptive Histogram Equalization Filter Based Early-Stage Breast Cancer Detection Techniques

Feature and Decision Fusion for Breast Cancer Detection

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