Abhishek Kumar Saxena,
Khushi Gupta,
Nikita Srivastava,
Shatakshi Chaurasia,
- Assistant Professor & HOD Department of IT, Bansal Institute of Engineering & Technology, Sitapur Road, Lucknow Uttar Pradesh India
- Student Department of IT, Bansal Institute of Engineering & Technology, Sitapur Road, Lucknow Uttar Pradesh India
- Student Department of IT, Bansal Institute of Engineering & Technology, Sitapur Road, Lucknow Uttar Pradesh India
- Student Department of IT, Bansal Institute of Engineering & Technology, Sitapur Road, Lucknow Uttar Pradesh India
Abstract
Skin cancer ranks among the most prevalent types of cancer globally and poses significant risks when left untreated. Skin cancer arises when abnormal cells proliferate uncontrollably in the skin. This uncontrolled growth can be triggered by genetic mutations, exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds, or various other factors. In this, the early detection of cancer plays a crucial role in treatment and reducing death rates. For early Detection, an automated and robust system is required to minimize the efforts, time, money and death rate. In recent years, advancements in artificial intelligence, particularly deep learning, and machine learning have shown promising results in various medical applications, including skin cancer detection. This research paper presents an automated skin cancer detection system based on deep learning and machine learning techniques. In this, both Machine learning and Deep learning techniques are used for early detection of skin cancer. The proposed system utilizes convolutional neural networks (CNNs) to analysing dermoscopic images and accurately classify skin lesions as benign or malignant. By leveraging a large dataset of images, from which the model learns to identify complex patterns and features which indicate different types of skin cancer. Additionally, the paper discusses the potential implications of deploying such a system in clinical settings, including improving diagnostic accuracy, reducing workload for healthcare professionals, and facilitating timely interventions for patients. This study investigates the creation of an automated skin cancer detection system utilizing the latest advancements in deep learning techniques.
Keywords: Skin cancer detection, Convolutional neural networks (CNNs), Dermoscopic images, medical applications, Healthcare automation, Deep learning.
[This article belongs to Research & Reviews: A Journal of Medicine(rrjom)]
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Research & Reviews: A Journal of Medicine
| Volume | 14 |
| Issue | 02 |
| Received | June 7, 2024 |
| Accepted | June 26, 2024 |
| Published | August 22, 2024 |