Melanoma Classification Through Deep Ensemble Learning and Explainable AI

• URL: http://arxiv.org/abs/2511.00246v1
• Date: Fri, 31 Oct 2025 20:36:12 GMT
• Title: Melanoma Classification Through Deep Ensemble Learning and Explainable AI
• Authors: Wadduwage Shanika Perera, ABM Islam, Van Vung Pham, Min Kyung An,
• Abstract summary: Melanoma is one of the most aggressive and deadliest skin cancers, leading to mortality if not detected and treated in the early stages.<n>System based on deep learning (DL) have been able to detect these lesions with high accuracy.<n>This paper proposes a machine learning model using ensemble learning of three state-of-the-art deep transfer Learning networks.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Melanoma is one of the most aggressive and deadliest skin cancers, leading to mortality if not detected and treated in the early stages. Artificial intelligence techniques have recently been developed to help dermatologists in the early detection of melanoma, and systems based on deep learning (DL) have been able to detect these lesions with high accuracy. However, the entire community must overcome the explainability limit to get the maximum benefit from DL for diagnostics in the healthcare domain. Because of the black box operation's shortcomings in DL models' decisions, there is a lack of reliability and trust in the outcomes. However, Explainable Artificial Intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This paper proposes a machine learning model using ensemble learning of three state-of-the-art deep transfer Learning networks, along with an approach to ensure the reliability of the predictions by utilizing XAI techniques to explain the basis of the predictions.

Related papers

• Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions [95.59915390053588]
  This study focuses on Explainable Artificial Intelligence (XAI) approaches-focusing on deep neural networks (DNNs) and large language models (LLMs)<n>We discuss critical symptoms that stem from deeper root causes (i.e., two paradoxes, two conceptual confusions, and five false assumptions)<n>To move beyond XAI's limitations, we propose a four-pronged paradigm shift toward reliable and certified AI development.
  arXiv  Detail & Related papers  (2026-02-27T16:58:27Z)
• The Impact of Lesion Focus on the Performance of AI-Based Melanoma Classification [0.0]
  We analyze the relationship between lesion attention and diagnostic performance.<n>Models with a higher focus on lesion areas achieved better diagnostic performance.<n>This study provides a foundation for developing more accurate and trustworthy melanoma classification models.
  arXiv  Detail & Related papers  (2026-01-01T14:17:32Z)
• Explainable AI as a Double-Edged Sword in Dermatology: The Impact on Clinicians versus The Public [46.86429592892395]
  explainable AI (XAI) addresses this by providing AI decision-making insight.<n>We present results from two large-scale experiments combining a fairness-based diagnosis AI model and different XAI explanations.
  arXiv  Detail & Related papers  (2025-12-14T00:06:06Z)
• KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs [39.47350988195002]
  Large language models (LLMs) have shown promise in leveraging language abilities and biomedical knowledge for diagnosis prediction.<n>We propose KERAP, a knowledge graph (KG)-enhanced reasoning approach that improves LLM-based diagnosis prediction through a multi-agent architecture.<n>Our framework consists of a linkage agent for mapping, a retrieval agent for structured knowledge extraction, and a prediction agent that iteratively refines diagnosis predictions.
  arXiv  Detail & Related papers  (2025-07-03T16:35:11Z)
• Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques [39.97900702763419]
  This study focuses on analyzing dermoscopy images to determine the depth of melanomas. The Breslow depth, measured from the top of the granular layer to the deepest point of tumor invasion, serves as a crucial parameter for staging melanoma and guiding treatment decisions. Various datasets, including ISIC and private collections, were used, comprising a total of 1162 images. Results indicated that the models achieved significant improvements over previous methods.
  arXiv  Detail & Related papers  (2024-06-19T11:07:55Z)
• Breast Cancer Diagnosis: A Comprehensive Exploration of Explainable Artificial Intelligence (XAI) Techniques [37.9243470221619]
  Article explores the application of Explainable Artificial Intelligence (XAI) techniques in the detection and diagnosis of breast cancer.<n>Aims to highlight the potential of XAI in bridging the gap between complex AI models and practical healthcare applications.
  arXiv  Detail & Related papers  (2024-06-01T18:50:03Z)
• An Interpretable Deep Learning Approach for Skin Cancer Categorization [0.0]
  We use modern deep learning methods and explainable artificial intelligence (XAI) approaches to address the problem of skin cancer detection. To categorize skin lesions, we employ four cutting-edge pre-trained models: XceptionNet, EfficientNetV2S, InceptionResNetV2, and EfficientNetV2M. Our study shows how deep learning and explainable artificial intelligence (XAI) can improve skin cancer diagnosis.
  arXiv  Detail & Related papers  (2023-12-17T12:11:38Z)
• Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method [0.0]
  This research paper focuses on Acute Lymphoblastic Leukemia (ALL), a form of blood cancer prevalent in children and teenagers. It proposes an automated detection approach using computer-aided diagnostic (CAD) models, leveraging deep learning techniques. The proposed method achieved an impressive 98.38% accuracy, outperforming other tested models.
  arXiv  Detail & Related papers  (2023-12-01T10:37:02Z)
• Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review [0.45545745874600063]
  "Ugly Duckling Naevus" comes into play when monitoring for melanoma, referring to a lesion with distinctive features. Computer-aided diagnosis (CAD) has become a significant player in the research and development field. This article extensively covers modern Machine Learning and Deep Learning algorithms for detecting melanoma and suspicious naevi.
  arXiv  Detail & Related papers  (2023-09-01T05:50:47Z)
• Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma [0.0]
  A lack of transparency in how artificial intelligence systems identify melanoma poses severe obstacles to user acceptance. Most XAI methods are unable to produce precisely located domain-specific explanations, making the explanations difficult to interpret. We developed an XAI system that produces text and region based explanations that are easily interpretable by dermatologists.
  arXiv  Detail & Related papers  (2023-03-17T17:25:55Z)
• Efficient Out-of-Distribution Detection of Melanoma with Wavelet-based Normalizing Flows [22.335623464185105]
  Melanoma is a serious form of skin cancer with high mortality rate at later stages. datasets are heavily imbalanced which complicates training current state-of-the-art supervised classification AI models. We propose to use generative models to learn the benign data distribution and detect Out-of-Distribution (OOD) malignant images through density estimation.
  arXiv  Detail & Related papers  (2022-08-09T09:57:56Z)
• Detecting Shortcut Learning for Fair Medical AI using Shortcut Testing [62.9062883851246]
  Machine learning holds great promise for improving healthcare, but it is critical to ensure that its use will not propagate or amplify health disparities. One potential driver of algorithmic unfairness, shortcut learning, arises when ML models base predictions on improper correlations in the training data. Using multi-task learning, we propose the first method to assess and mitigate shortcut learning as a part of the fairness assessment of clinical ML systems.
  arXiv  Detail & Related papers  (2022-07-21T09:35:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.