Skin Cancer Detection Research Topics

Skin cancer Detection Research Topics is used to identify the skin cancer it finds irregularities or injuries in the skin that denote the presence of cancer. Below we provide the concepts and the extensive information about the detection of skin cancer.

1. Define Skin Cancer Detection

At the first stage we first see the definition for skin cancer detection; it is the finding and analysis of unusual skin injuries by utilizing imaging technologies and visual examination, essential for the early interference and enhanced prediction.

2. What is Skin Cancer Detection?

Next to the definition we see the detailed explanation of detecting skin cancer; it includes the analysis and finding irregularities or injuries in skin which denotes the existence of cancer growth like basal cell carcinoma, squamous cell carcinoma or melanoma. This process generally contains dermoscopy and possible biopsy, visual analysis or imaging tests to validate the analysis. Early identification is important for efficient treatment and enhanced findings.

3. Where Skin Cancer Detection used?

After the detailed explanation we discuss where to utilize this proposed strategy. It is utilized in the places like Occupational Health, Self-Examination, Primary Care Clinics, Dermatology Clinics, Research and Clinical Trials, Hospitals, Teledermatology, Mole Mapping Clinics and Skin Cancer Screenings are the places that our proposed skin cancer detection is utilized.

4. Why Skin Cancer Detection technology proposed? , previous technology issues

Identification of skin cancer is proposed in this work, it is proposed to identify and analyze the cancer on skin at a starting stage. Due to the enhanced sun exposure and aging populations are compelling skin cancer rates. To detect it in the early stage is an efficient treatment and technology can scale dermatologist accessibility, particularly in poor regions. The existing technology has the issues as follows: lack of interpretability, lack of accuracy and time consuming.

5. Algorithms / protocols

For this research the skin cancer detection some of the existing technology issues are overcome by this proposed research. Here are some of the algorithms to be used in this research are RESNET 50, Stochastic Gradient Descent (SGD) algorithm, Snake model of active contour and CapsNet model are the methods to be utilized for this research.

6. Comparative Study / Analysis

We compare the methods in this research to obtain the accurate possible outcome when compared to the existing research. Some of the methods that we compared are:

• In skin cancer detection for feature extraction, the ResNet50 transfer learning method is used. It highly improves the robustness and efficacy of skin cancer detection methods and it particularly gives assistance with the limited data.
• We utilize gradient based optimization techniques like Stochastic Gradient Descent (SGD), to optimize the model’s parameter.
• The CapsNet model is used for classification and it allows the model to particularly allow some part of input images, and provide high significance to the more relevant data for the image.

7. Simulation results / parameters

In this research we detect skin cancer and overcome some existing technology issues. Here we compare some performance metrics to obtain the possible result. The metrics that we compared are: F1 score, Sensitivity, Accuracy, Recall, AUC-ROC and specificity are the metrics which are compared for this research to get the accurate result.

8. Dataset LNKS / Important URL

To identify and examine cancer on skin at the initial stage, we propose this research moreover it addresses several previous technology issues. The succeeding we provide are the links that is effectively helpful when we go through the skin cancer detection research:

• https://www.researchgate.net/profile/Tahira-Nazir-2/publication/353705451_Skin_cancer_detection_from_dermoscopic_images_using_deep_learning_and_fuzzy_k-means_clustering/links/612dbf192b40ec7d8bd59fa9/Skin-cancer-detection-from-dermoscopic-images-using-deep-learning-and-fuzzy-k-means-clustering.pdf
• https://www.sciencedirect.com/science/article/pii/S2772662223001182

9. Skin Cancer Detection Applications

The applications that skin cancer detection that widely utilized in the places like Mole mapping, Research and Clinical Trials, Visual Inspection, Dermoscopy, Community Screenings, Total Body Photography, Computer-Aided Detection (CAD), Skin Biopsy, Mobile Apps and Teledermatology are the applications for the detection of skin cancer.

10. Topology for Skin Cancer Detection

Topology that is employed for the skin cancer detection are Pre-processing, Validation and Testing, Visual Inspection, Classification, Feature Extraction and segmentation are the topology used for this proposed research.

11. Environment in Skin Cancer Detection

Now we see the environment for the research skin cancer detection. Occupational Exposure, sun Exposure, Ultraviolet (UV) Radiation, Sunburns, Geographical Location, Family History, Climate, Artificial UV sources, Altitude, Clothing and Sunscreen Use, Outdoor Activities and Environmental Pollution are the environments that used for our proposed research.

12. Simulation Tools

Skin cancer detection is proposed to come across several difficulties in the existing research. Here we see the software requirements that were needed for his research as follows.  The tool that is used to implement the research is Python 3.11.4. Then the operating system that is required for this research is Windows – 10 (64 – bit).

13. Results

In this we proposed a skin cancer detection to examine and detect the disease at the early stage and is identified by using the imaging technologies and visual examination, moreover it is now being used in many applications. We have to compare the various existing technologies performance metrics to attain the best findings for our research.

Skin Cancer Detection Research Ideas:

The following are the research topics which are relevant to skin cancer detection, which are useful to us when we have any queries about this proposed research.

1. Design of a Skin Cancer Detection Classification with Python GUI and Tensorflow
2. Skin Cancer Detection and Control Techniques Using Hybrid Deep Learning Techniques
3. Skin Cancer Detection using Ensemble Learning
4. Study of Skin Cancer Detection Using Images: A Literature Review
5. Early Stage Skin Cancer Detection Using Image Processing
6. Using Effective Medium Theory to Simulate Skin Cancer Detection with a Substrate-Integrated Waveguide Probe
7. Alternating Sequential and Residual Networks for Skin Cancer Detection from Biomedical Images
8. Skin Cancer Detection Using Convolution Neural Network
9. Skin Cancer Detection Using CNN
10. Artificial Intelligence Based Real-Time Skin Cancer Detection
11. Resnet 50 Based Classification Model for Skin Cancer Detection Using Dermatoscopic Images
12. A Skin Cancer Detection System Based on CNN with Hair Removal
13. A New Hybrid-shaped Complementry Split Ring Resonator for Skin Cancer Detection
14. Skin Cancer Detection using Convolutional Neural Network
15. Skin Cancer Detection and Intensity Calculator using Deep Learning
16. Detection of Skin Cancer Using Artificial Intelligent Techniques
17. A Review on Preprocessing, Segmentation and Classification Techniques for Detection of Skin Cancer
18. Classification And Detection Of Skin Cancer Using Deep Learning Methods
19. The Detection of Skin Cancer and Oral Cancer : A comparison of the proposed Hybrid Model with the Existing Detection Algorithms
20. An Optimized Predictive Model Based on Deep Neural Network for Detection of Skin Cancer and Oral Cancer
21. Skin Cancer Detection Using Deep Learning Technique
22. Enhancing Dermoscopic Skin Cancer Detection via Hair Artifact Removal: An Iterative Diffusion Model Approach
23. Automated Skin Cancer Detection using Deep Learning with Self-Attention Mechanism
24. Investigation Of Effective Medium Theory Concerning Applications For Skin Cancer Detection
25. A Comparative Study of Ensemble Deep Learning Models for Skin Cancer Detection
26. Enhancing Multi-Class Skin Lesion Classification with Modified EfficientNets: Advancing Early Detection of Skin Cancer
27. Skin Cancer Detection Using Multi Class CNN Algorithm
28. A Prototype Design for the Detection of Skin Cancer Types Using Tensorflow
29. Circular Patch with Three Circular Slots and Defected Ground UWB Antenna Sensor for Early-Stage Skin Cancer Detection
30. Automation of Skin Cancer Detection with Image Processing Using Efficient and Lightweight CNN Models
31. Deep Learning Architecture Based Skin Cancer Detection Using Deep Belief Network and Grey Wolf Optimization
32. Detection of Skin Cancer Using SFLA Based Apex Component Analysis Network
33. Skin Cancer Detection Using Semi-Unsupervised Deep Learning in Generative Adversarial Networks: A Study on Melanoma Detection
34. Development of a 3D Printed Dual-Band mmWave and THz Near-Field Microscope for Skin Cancer Detection
35. SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods
36. An Effective Method for Skin Cancer Detection using Convolutional Kernel Extreme Learning Machine
37. Skin Cancer Detection using Machine Learning Framework with Mobile Application
38. Melanoma Skin Cancer Detection using a CNN-Regularized Extreme Learning Machine (RELM) based Model
39. Skin Cancer Detection using VGG16, InceptionV3 and ResUNet
40. Melanlysis: A mobile deep learning approach for early detection of skin cancer
41. Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network
42. A full-resolution convolutional network with a dynamic graph cut algorithm for skin cancer classification and detection
43. European Cancer Summit 2022 submission for Digital Health Network The impact of an artificial intelligence (AI) based app for skin cancer detection: a first clinical practice evaluation in a population-based setting
44. Enhanced Skin Cancer Classification with AlexNet and Transfer Learning
45. Performance Evaluation of Oversampling Methods on Deep Learning-Based Skin Cancer Classification
46. An Architecture for Microprocessor-Executable Skin Cancer Classification
47. Enhancing Skin Cancer Diagnosis Using DL with 3D Model
48. Automatic Segmentation and Classification of Skin Cancer Cells using Thresholding and Deep Learning based Techniques
49. An Ensemble Learning Approach For Improved Skin Cancer Classification
50. Melanoma Mirage: Unmasking Skin Cancer with Deep Learning