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AI vs. Skin Cancer
AI vs. skin cancer? Melanoma diagnoses advance? When I go see my dermatologist, I point to the new moles that I think might be melanoma or various forms of skin cancer, and he studies them, telling me if he is worried about any of them. He has removed a couple, wanting to have a pathologist weigh in. I’ve been diagnosed with basal cell carcinoma and a couple of indeterminate spots.
I had an autologous stem cell transplant when I was diagnosed with a blood cancer called multiple myeloma. According to research, an ASCT increased my risk of a diagnosis of skin cancer.
According to the research below, the process outlined above might have let some melanomas go undiagnosed.
AI vs. Skin Cancer
Studying complementary cancer therapies since my own cancer diagnosis in 1994 has taught me that there are a host of evidence-based, non-conventional therapies that reduce my increased risk of skin cancer.
Have you been diagnosed with skin cancer? What type? What stage? Scroll down the page, post a question or a comment, and I will reply to you ASAP.
David Emerson
- Cancer Survivor
- Cancer Coach
- Director PeopleBeatingCancer
Fusion of metadata and dermoscopic images for melanoma detection: Deep learning and feature importance analysis
Abstract
In the era of smart healthcare, integrating multimodal data is essential for improving diagnostic accuracy and enabling personalized care. This study presented a deep learning-based multimodal approach for melanoma detection, leveraging both dermoscopic images and clinical metadata to enhance classification performance.
The proposed model integrated a multi-layer convolutional neural network (CNN) to extract image features and combined them with structured metadata, including patient age, gender, and lesion location, through feature-level fusion. The fusion process occurred at the final CNN layer, where high-dimensional image feature vectors were concatenated with processed metadata.
The metadata was handled separately through a fully connected neural network comprising multiple dense layers. The final fused representation was passed through additional dense layers, culminating in a classification layer that outputted the probability of melanoma presence. The model was trained end-to-end using the SIIM-ISIC dataset, allowing it to learn a joint representation of image and metadata features for optimal classification.
Various data augmentation techniques were applied to dermoscopic images to mitigate class imbalance and improve model robustness. Additionally, exploratory data analysis (EDA) and feature importance analysis were conducted to assess the contribution of each metadata feature to the overall classification.
Our fusion-based deep learning architecture outperformed single-modality models, boosting classification accuracy. The presented model achieved an accuracy of 94.5% and an overall F1-score of 0.94, validating its effectiveness in melanoma detection. This study aims to highlight the potential of deep learning-based multimodal fusion in enhancing diagnostic precision, offering a scalable and reliable solution for improved melanoma detection in smart healthcare systems…
Conclusion and future work
This study presented a deep learning-based multi-sensor fusion model for melanoma detection that integrates dermoscopic images and clinical metadata for a more comprehensive diagnosis. The presented model achieved a high accuracy of 94.5%. The results demonstrate that fusing image and metadata features at the model level improves classification performance compared to single-modality models. By addressing the challenges of class imbalance and incorporating feature importance analysis, this
AI vs. skin cancer AI vs. skin cancer AI vs. skin cancer