Early Detection and Classification of Breast Cancer Using Deep Learning Techniques

• URL: http://arxiv.org/abs/2501.12217v1
• Date: Tue, 21 Jan 2025 15:39:29 GMT
• Title: Early Detection and Classification of Breast Cancer Using Deep Learning Techniques
• Authors: Mst. Mumtahina Labonno, D. M. Asadujjaman, Md. Mahfujur Rahman, Abdullah Tamim, Mst. Jannatul Ferdous, Rafi Muttaki Mahi,
• Abstract summary: Breast cancer is one of the deadliest cancers causing massive number of patients to die annually all over the world according to the WHO. Using automation for early-age detection of breast cancer, Artificial Intelligence and Machine Learning technologies can be implemented for the best outcome.
• Score: 0.0
• License:
• Abstract: Breast cancer is one of the deadliest cancers causing about massive number of patients to die annually all over the world according to the WHO. It is a kind of cancer that develops when the tissues of the breast grow rapidly and unboundly. This fatality rate can be prevented if the cancer is detected before it gets malignant. Using automation for early-age detection of breast cancer, Artificial Intelligence and Machine Learning technologies can be implemented for the best outcome. In this study, we are using the Breast Cancer Image Classification dataset collected from the Kaggle depository, which comprises 9248 Breast Ultrasound Images and is classified into three categories: Benign, Malignant, and Normal which refers to non-cancerous, cancerous, and normal images.This research introduces three pretrained model featuring custom classifiers that includes ResNet50, MobileNet, and VGG16, along with a custom CNN model utilizing the ReLU activation function.The models ResNet50, MobileNet, VGG16, and a custom CNN recorded accuracies of 98.41%, 97.91%, 98.19%, and 92.94% on the dataset, correspondingly, with ResNet50 achieving the highest accuracy of 98.41%.This model, with its deep and powerful architecture, is particularly successful in detecting aberrant cells as well as cancerous or non-cancerous tumors. These accuracies show that the Machine Learning methods are more compatible for the classification and early detection of breast cancer.

Related papers

• Analysis of Transferred Pre-Trained Deep Convolution Neural Networks in Breast Masses Recognition [3.3686252536891454]
  The effect of layer freezing in a pre-trained CNN is investigated for breast cancer detection by classifying mammogram images as benign or malignant. The best recognition rate was obtained from a frozen first block of VGG19 with a sensitivity of 95.64 %, while the training of the entire VGG19 yielded 94.48%.
  arXiv  Detail & Related papers  (2024-12-23T20:16:45Z)
• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Computer Aided Detection and Classification of mammograms using Convolutional Neural Network [0.0]
  Breast cancer is one of the most major causes of death among women, after lung cancer. Deep learning or neural networks are one of the methods that can be used to distinguish regular and irregular breast identification. CNNM dataset has been used in which nearly 460 images are of normal and 920 of abnormal breasts.
  arXiv  Detail & Related papers  (2024-09-04T03:42:27Z)
• Improving Breast Cancer Grade Prediction with Multiparametric MRI Created Using Optimized Synthetic Correlated Diffusion Imaging [71.91773485443125]
  Grading plays a vital role in breast cancer treatment planning. The current tumor grading method involves extracting tissue from patients, leading to stress, discomfort, and high medical costs. This paper examines using optimized CDI$s$ to improve breast cancer grade prediction.
  arXiv  Detail & Related papers  (2024-05-13T15:48:26Z)
• Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
  The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023. The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy. In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
  arXiv  Detail & Related papers  (2023-04-12T15:08:34Z)
• A Multi-Institutional Open-Source Benchmark Dataset for Breast Cancer Clinical Decision Support using Synthetic Correlated Diffusion Imaging Data [82.74877848011798]
  Cancer-Net BCa is a multi-institutional open-source benchmark dataset of volumetric CDI$s$ imaging data of breast cancer patients. Cancer-Net BCa is publicly available as a part of a global open-source initiative dedicated to accelerating advancement in machine learning to aid clinicians in the fight against cancer.
  arXiv  Detail & Related papers  (2023-04-12T05:41:44Z)
• A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images [0.0]
  A Deep Neural Network (DNN) is commonly employed to improve accuracy and breast cancer detection. We have analyzed pre-trained deep transfer learning models for detecting breast cancer using the 2453 histopathology images dataset. After analyzing the transfer learning model, we found that ResNet50 outperformed other models, achieving accuracy rates of 90.2%, Area under Curve (AUC) rates of 90.0%, recall rates of 94.7%, and a marginal loss of 3.5%.
  arXiv  Detail & Related papers  (2023-04-11T07:17:55Z)
• Classification of Breast Tumours Based on Histopathology Images Using Deep Features and Ensemble of Gradient Boosting Methods [0.0]
  Deep feature transfer learning is used as the main idea of the proposed CAD system's feature extractor. Inception-ResNet-v2 has shown the best feature extraction capability in the case of breast cancer histopathology images. In the classification phase, the ensemble of CatBoost, XGBoost and LightGBM has provided the best average accuracy.
  arXiv  Detail & Related papers  (2022-09-03T09:27:00Z)
• EMT-NET: Efficient multitask network for computer-aided diagnosis of breast cancer [58.720142291102135]
  We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously. We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions. The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
  arXiv  Detail & Related papers  (2022-01-13T05:24:40Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Comparison of different CNNs for breast tumor classification from ultrasound images [12.98780709853981]
  classifying benign and malignant tumors from ultrasound (US) imaging is a crucial but challenging task. We compared different Convolutional Neural Networks (CNNs) and transfer learning methods for the task of automated breast tumor classification. The best performance was obtained by fine tuning VGG-16, with an accuracy of 0.919 and an AUC of 0.934.
  arXiv  Detail & Related papers  (2020-12-28T22:54:08Z)

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.