Skin Cancer Detection Using Convolutional Neural Network

Skin cancer detection through the use of Convolutional Neural Networks (CNNs) has emerged as important research in current trends. Early and more exact identification of melanoma and other skin cancers are accomplished through manipulating the efficiency of deep learning to help dermatologists. By using our massive resources, we carry out your research work by sharing trending ideas and as per your specifications. By using a robust plagiarism tool, we assure that all your work will be original. Our team are always reliable  that meets up all  your demand. Here, we describe the procedural flow to achieve this project:

  1. Gathering of Data:
  • Dataset: In the data gathering phase, we acquire a skin lesion image dataset. Our project considers the International Skin Imaging Collaboration (ISIC) Archive dataset that provides various labeled skin lesion images.
  • Annotations: Make sure that our dataset images are defined with their related types such as malignant, nevus, melanoma, benign, etc,
  1. Pre-processing of Data:
  • Image Resizing: Pre-process the data by resizing all images to a balanced dimension. For instance: When we are dealing with InceptionV3 framework, resize the images to 299×299 pixels and for VGG16 framework, resize to 224×224.
  • Data Augmentation: To enhance the dataset dimension and avoid overfitting issue, our work augments the dataset by creating altered versions of images through the process of scaling, flipping and rotation.
  • Normalization: Based on our pre-trained framework’s need, normalize the image pixel values ranging from [0 to 1] or [-1 to 1].
  1. Model Building:
  • Pre-trained Models: By employing transfer learning, we manipulate frameworks such as VGG16, MobileNet, ResNet or InceptionV3 which are trained on huge datasets such as ImageNet and it also offers better initializations for weights.
  • Fine-tuning: Our research carries out the fine-tuning process by changing the top layers of the above-mentioned frameworks and concatenating our fully connected layers, ending with softmax activation function with as enormous neurons as there are classes.
  • Regularization: To avoid overfitting problems, we employ dropout or batch normalization layers.
  1. Training of Model:
  • Loss Function: For binary categorization (example: malignant vs. benign), our work utilizes binary crossentropy and utilizes categorical crossentropy for multi-class categorization.
  • Optimizer: We employ various optimizers like RMSprop, Adam, or SGD.
  • Batch Size & Epochs: Alter the batch size and epochs based on the dimension of our dataset and computing resources. To determine the number of epochs, we track the validation loss and accuracy.
  • Callbacks: For robust training, our approach considers callbacks such as model checkpointing, learning rate schedulers and early stopping.
  1. Evaluation:
  • Accuracy: By using test data, we estimate accuracy. But accuracy alone is not adequate for clinical applications.
  • Sensitivity & Specificity: To reduce false positives and false negatives, then consideration of these metrics is essential in our clinical diagnosis-based research.
  • ROC & AUC: To interpret our framework’s efficiency throughout various thresholds, we use the Receiver Operating Characteristics (ROC) curve and Area Under Curve (AUC).
  1. Post-Processing:
  • Thresholding: Adapt the decision boundary depending on our application’s requirements (like being more conventional in identifying malignancy) rather than setting default threshold value (0.5).
  • Ensemble Models: To accomplish efficient outcomes, we integrate forecastings from various frameworks.
  1. Deployment:

If we aim for actual-world applications, implement our framework on mobile devices by utilizing environments such as TensorFlow Lite or even on a server for API approach. 

Skin Cancer Detection Using Machine Learning

Skin Cancer Detection Research Topics

  1. Analysis of Skin Lesions for Cancer Detection Using Convolutional Neural Networks
  2. Skin Cancer Detection using Ensemble Learning
  3. Skin Cancer Detection Using Deep Learning Technique
  4. Alternating Sequential and Residual Networks for Skin Cancer Detection from Biomedical Images
  5. A Skin Cancer Detection System Based on CNN with Hair Removal
  6. Early Stage Skin Cancer Detection Using Image Processing
  7. Artificial Intelligence Based Real-Time Skin Cancer Detection
  8. Skin Cancer Detection using Convolutional Neural Network
  9. Resnet 50 Based Classification Model for Skin Cancer Detection Using Dermatoscopic Images
  10. Automated Skin Cancer Detection using Deep Learning with Self-Attention Mechanism
  11. Skin Cancer Detection using VGG16, InceptionV3 and ResUNet
  12. Classification And Detection Of Skin Cancer Using Deep Learning Methods
  13. Investigation Of Effective Medium Theory Concerning Applications For Skin Cancer Detection
  14. The Detection of Skin Cancer and Oral Cancer: A comparison of the proposed Hybrid Model with the Existing Detection Algorithms
  15. Skin Cancer Detection and Intensity Calculator using Deep Learning
  16. Skin Cancer Detection Using Multi Class CNN Algorithm
  17. Skin Cancer Detection and Control Techniques Using Hybrid Deep Learning Techniques
  18. Circular Patch with Three Circular Slots and Defected Ground UWB Antenna Sensor for Early-Stage Skin Cancer Detection
  19. Automation of Skin Cancer Detection with Image Processing Using Efficient and Lightweight CNN Models
  20. SKIN_ML: An Efficient Approach for Skin Cancer Detection Using Soft Computing Methods
  21. Melanlysis: A mobile deep learning approach for early detection of skin cancer
  22. Study of Skin Cancer Detection Using Images: A Literature Review
  23. A Review on Preprocessing, Segmentation and Classification Techniques for Detection of Skin Cancer
  24. Deep Learning Architecture Based Skin Cancer Detection Using Deep Belief Network and Grey Wolf Optimization
  25. Using Effective Medium Theory to Simulate Skin Cancer Detection with a Substrate-Integrated Waveguide Probe
  26. Skin Cancer Detection using Machine Learning Framework with Mobile Application
  27. Detection of Skin Cancer Using SFLA Based Apex Component Analysis Network
  28. An Optimized Predictive Model Based on Deep Neural Network for Detection of Skin Cancer and Oral Cancer
  29. Enhancing Multi-Class Skin Lesion Classification with Modified EfficientNets: Advancing Early Detection of Skin Cancer
  30. A Comparative Study of Ensemble Deep Learning Models for Skin Cancer Detection
  31. An Effective Method for Skin Cancer Detection using Convolutional Kernel Extreme Learning Machine
  32. Design of a Skin Cancer Detection Classification with Python GUI and Tensorflow
  33. Detection of Skin Cancer Using Artificial Intelligent Techniques
  34. Melanoma Skin Cancer Detection using a CNN-Regularized Extreme Learning Machine (RELM) based Model
  35. Development of a 3D Printed Dual-Band mmWave and THz Near-Field Microscope for Skin Cancer Detection
  36. Skin cancer detection from dermoscopic images using Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm
  37. Transfer Learning with Ensembles of Deep Neural Networks for Skin Cancer Detection in Imbalanced Data Sets
  38. Internet of Things-Assisted Smart Skin Cancer Detection Using Metaheuristics with Deep Learning Model
  39. Model hybridization & learning rate annealing for skin cancer detection
  40. A novel hybrid Extreme Learning Machine and Teaching–Learning-Based​ Optimization algorithm for skin cancer detection

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