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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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Issue: May 2024
Volume 9 | Issue 5
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| Paper Title: | Automated breast cancer diagnosis using deep learning model |
| Authors Name: | Seema Keshri |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_213746
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| Published Paper Id: | IJNRD2402268 |
| Published In: | Volume 9 Issue 2, February-2024 |
| DOI: | |
| Abstract: | Breast cancer is one of the leading causes of death among women. There has been a lot of research done on the use of different image processing and classification algorithms for the diagnosis and detection of breast cancer. An automated breast cancer diagnosis system using a deep learning model involves training a neural network on a dataset of mammogram images, along with their corresponding labels (i.e. benign or malignant). The model can then be used to classify new mammogram images as either benign or malignant. A thorough experimental setup is constructed in order to train and test the CNN model. The dataset is split into training and testing sets, with the training set being enhanced using the proper data augmentation techniques. The approach for training deep learning models for breast cancer diagnosis is to use convolutional neural networks (CNNs), which are well-suited for image classification tasks. These models can learn to extract features from the mammogram images and use these features to make predictions. The use of CNN model helps to incorporate a lot more data which helps the model become more generic and makes the diagnosis more reliable. The outcomes show how successful the suggested CNN-based method is for finding breast cancer. The trained model exhibits good performance in differentiating between malignant and benign lesions and achieves high accuracy. |
| Keywords: | breast cancer detection, deep learning, convolutional neural network, training dataset, test dataset mammogram images. |
| Cite Article: | "Automated breast cancer diagnosis using deep learning model", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 2, page no.c612-c622, February-2024, Available :http://www.ijnrd.org/papers/IJNRD2402268.pdf |
| Downloads: | 000118760 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2402268 Registration ID: 213746 Published In: Volume 9 Issue 2, February-2024 DOI (Digital Object Identifier): Page No: c612-c622 Country: Bangalore North, Karnataka, India Research Area: Health Science Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2402268 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2402268 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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