Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides. Furthermore, because of the massive size of most whole-slide images (WSI), it is wise to divide a slide into batches of small image patches and apply methods independently on each patch. The present work introduces a novel method for the automated diagnosis and detection of metastases from whole slide images using the Fast AI framework and the 1-cycle policy. Additionally, it compares this new approach to previous methods. The proposed model has surpassed other state-of-art methods with more than 97.4% accuracy. In addition, a mobile application is developed for prompt and quick response. It collects user information and models to diagnose metastases present in the early stages of cancer. These results indicate that the suggested model may assist general practitioners in accurately analyzing breast cancer situations, hence preventing future complications and mortality. With digital image processing, histopathologic interpretation and diagnostic accuracy have improved considerably.

https://www.mdpi.com/1424-8220/22/8/2988/pdf?version=1649916576

Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy

The cancer is the most dangerous diseases in the world, its mainly effective for women. So, our prime target must be curing the cancer through scientific investigation and the second main target should be early detection of cancer because the early detection of cancer can be helpful for remove the cancer completed. After reviewed 41 papers we found that several techniques are available for cancer detection. In this paperwe proposedDeep Leaning algorithm convolutional neural network for diagnosed breast cancer using Mammograph MIAS database. The paper shows how we can use deep learning technology for diagnosis breast cancer using MIAS Dataset. Because deep learning techniques almost used for high task objective Computer Vision, Image processing, Medical Diagnosis, Neural Language Processing. But in this paper, we are applying deep learning technology on the MIAS Database and we have seen that is very beneficial for us for diagnosis breast cancer with accuracy 98%. This paper is divided in three parts first we have collect dataset and applied pre-processing algorithm for scaled and filter data then we have split dataset in training and testing purpose and generate some graph for visualization data. In last implement model on training dataset and achieved accuracy 98%. So, we have seen deep learning technology is a good way for diagnosis breast cancer with MIAS Dataset. This database provides200images and 12 features in the dataset. In this paper we have used 12 features for diagnosis breast cancer that we have got after pre-processing. But before train model we have applied some pre-processing algorithm like Watershed Segmentation, Colour based segmentationand Adaptive Mean Filtersfor scaled dataset then applied model and achieved accuracy. In this paper we also compare deep learning algorithm with other machine learning and seen our proposed system is proved best from others machine learning algorithm.

Breast Cancer Detection From Histopathological Images Using Deep Learning

The cancer is the most dangerous diseases in the world, its mainly effective for women. So, our prime target must be curing the cancer through scientific investigation and the second main target should be early detection of cancer because the early detection of cancer can be helpful for remove the cancer completed. After reviewed 41 papers we found that several techniques are available for cancer detection. In this paperwe proposedDeep Leaning algorithm neural network for diagnosed breast cancer using Wisconsin Breast Cancer database. The paper shows how we can use deep learning technology for diagnosis breast cancer using UCI Dataset. Because deep learning techniques almost used for high task objective Computer Vision, Image processing, Medical Diagnosis, Neural Language Processing. But in this paper, we are applying deep learning technology on the Wisconsin Breast Cancer Database and we have seen that is very beneficial for us for diagnosis breast cancer with accuracy 99.67%. This paper is divided in three parts first we have collect dataset and applied pre-processing algorithm for scaled and filter data then we have split dataset in training and testing purpose and generate some graph for visualization data. In last implement model on training dataset and achieved accuracy 99.67%. So, we have seen deep learning technology is a good way for diagnosis breast cancer with Wisconsin Breast Dataset. This database provides 569 rows and 30 features in the dataset. In this paper we have used 11 features for diagnosis breast cancer that we have got after pre-processing. But before train model we have applied some pre-processing algorithm like Label Encoder, Normalizerand StandardScalerfor scaled dataset then applied model and achieved accuracy. In this paper we also compare deep learning algorithm with other machine learning and seen our proposed system is proved best from others machine learning algorithm.

Breast Cancer Diagnosis Using Deep Learning Algorithm

Data mining (DM) consists in analysing a set of observations to find unsuspected relationships and then summarising the data in new ways that are both understandable and useful. It has become widel...

Classification techniques in breast cancer diagnosis: A systematic literature review

Region-of-interest (ROI) segmentation is an important critical step and challenging task in the evolution of computer-aided detection (CAD) system for breast cancer. The discovery of breast cancer in early stages can save many women lives. However, most of the early detection systems are costly in terms of complexity, price and processing time; that make it unsuited for developing countries. The digital mammography is proven to be one of the most important diagnostic techniques for breast cancer tumors. Therefore, this work proposes a CAD system for breast cancer detection from digital mammography based on Gaussian Mixture Model (GMM) followed by Support Vector Machine (SVM). The best contribution of our proposed system is the usage of GMM for the first time in the literature for mammogram images segmentation into ROI areas. Besides, the discrimination between the three classes of tissues as normal, benign or malignant, is used without previous knowledge of mammogram images’ type. Moreover, the proposed system is fully automated in all of its stages with reduced computation compared with recent used methods. Hence, it offers a suitable early detection system to our country regarding moneywise, timewise, and reduced complexity. A non-linear multi-class SVM is used for classifying the ROI into three classes: normal, benign or malignant tissue. The experiments show overall average classification accuracy of 90% for detecting normal, malignant or benign on randomly chosen 90 cases from the benchmark mini-MIAS dataset. On the other hand, the proposed method achieves 92.5% accuracy when classifying the benign from malignant cases.

https://ajnsa.journals.ekb.eg/article_29187_b0670a53e2c1c5a3e3f81709b20bd5c0.pdf

Computer-Aided Detection System for Breast Cancer Based on GMM and SVM

Neural Network is utilized as a developing analytic tool for the diagnosis of breast cancer. The goal of this research is to determine breast tumor from digital mammograms with a machine learning technique in view of RF and combination of RF-ELM classifier. For digital mammogram images, MIAS database is used. Preprocessing is usually needed to enhance the low quality of the image. The region of interest (ROI) is determined in line with the scale of suspicious region. After the suspicious area is sectioned, features are extracted by texture analysis. GLCM is used as a texture attribute to extract the suspicious area. From all extracted features best features are selected with the help of CBF method. To enhance the exactness of classification, only six features are selected. These features are mean, standard deviation, kurtosis, variance, entropy and correlation coefficient. RF and RF-ELM are used as a classifier. The outcomes of present work show that the CAD system with the usage of RF-ELM classifier may be very powerful and achieves the exceptional results in the prognosis of breast cancer.

R. D. Ghongade

Papers

Detection and classification of breast cancer from digital mammograms using RF and RF-ELM algorithm