mlsanityData to 14 Oct 2026

Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection

arXiv 2504.13186 ↗First posted 30 Mar 2025eess.IV cs.AI cs.CVcited by 4

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Abstract (arXiv)

The rapid advancement of deep learning (DL) has transformed healthcare, particularly in cancer detection and diagnosis. DL surpasses traditional machine learning and human accuracy, making it a critical tool for identifying diseases. Despite numerous reviews on DL in healthcare, a comprehensive analysis of its role in cancer detection remains limited. Existing studies focus on specific aspects, leaving gaps in understanding its broader impact. This paper addresses these gaps by reviewing advanced DL techniques, including transfer learning (TL), reinforcement learning (RL), federated learning (FL), Transformers, and large language models (LLMs). These approaches enhance accuracy, tackle data scarcity, and enable decentralized learning while maintaining data privacy. TL adapts pre-trained models to new datasets, improving performance with limited labeled data. RL optimizes diagnostic pathways and treatment strategies, while FL fosters collaborative model development without sharing sensitive data. Transformers and LLMs, traditionally used in natural language processing, are now applied to medical data for improved interpretability. Additionally, this review examines these techniques' efficiency in cancer diagnosis, addresses challenges like data imbalance, and proposes solutions. It serves as a resource for researchers and practitioners, providing insights into current trends and guiding future research in advanced DL for cancer detection.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 51 of its 227 references that are in the map. First posted on arXiv 30 Mar 2025.

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51 of 227 references.

References · 51

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38 more
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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025Image Segmentation with Large Language Models: A Survey with Perspectives for Intelligent Transportation Systemscited by 2
  2. 2025Multi-Stage Retrieval for Operational Technology Cybersecurity Compliance Using Large Language Models: A Railway Casestudycited by 1
  3. 2025Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Studycited by 1
  4. 2025A Novel Hybrid Deep Learning and Chaotic Dynamics Approach for Thyroid Cancer Classificationcited by 0

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  6. 2025DCSNet: A Lightweight Knowledge Distillation-Based Model with Explainable AI for Lung Cancer Diagnosis from Histopathological Images
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