Nowadays, the classification of medical images has become an essential part of identifying the disease. Among various existing critical diseases, identification of breast cancer has now come up with the topic of investigation. To identify the affected regions of the images, a deep learning-based ...
Nowadays, the classification of medical images has become an essential part of identifying the disease. Among various existing critical diseases, identification of breast cancer has now come up with the topic of investigation. To identify the affected regions of the images, a deep learning-based app...
Worldwide, according to the Cancer prevision Organization American Institute of Cancer Research 2020 and National Breast Cancer Foundation, INC report [1], breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. In America, according to the American C...
Breast cancer Classification algorithms Deep learning Histopathological images Machine learning Mammogram IoT 5G 1. Introduction Cancer is one of the most common and deadly disease around the world and it has numerous different types. Recently, studies proved that Breast Cancer (BC) is the most preval...
The breast cancer dataset from Kaggle is utilized. The test and training data were divided by 7:3. Important features are determined by the correlation matrix. Metrics found the most effective classification models after creating the models. The results of future optimization techniques will be ...
Mercan, Mehta, Bartlett, Shapiro, Weaver and Elmore (2019) [26] proposed a breast cancer classification framework based on biopsy images. They generated small patches from the images and trained a CNN model to generate patch-level tissue labels. Then, the image-level tissue labels can be ...
We propose an effective machine learning approach to identify group of interacting single nucleotide polymorphisms (SNPs), which contribute most to the breast cancer (BC) risk by assuming dependencies among BCAC iCOGS SNPs. We adopt a gradient tree boost
One of the most frequent causes of death for women worldwide is breast cancer. In most cases, breast cancer can be quickly identified if certain symptoms e
However, deep learning methods require a large amount of labeled data for classification. In this study, we present a few-shot learning approach for the classification of ultrasound breast cancer images using meta-learning methods. We used prototypical networks and model agnostic meta-learning (MAML...
of four magnification levels. Each image in this dataset is of size 752\(\times\)582. A sample of the dataset is presented in Figure2. Due to its relatively large number of samples, the dataset is commonly used as the benchmark dataset in breast cancer classification37. Table2presents a ...