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14 pages, 2562 KB

Open AccessArticle

Enhancing Melanoma Diagnosis with Advanced Deep Learning Models Focusing on Vision Transformer, Swin Transformer, and ConvNeXt

by Serra Aksoy, Pinar Demircioglu and Ismail Bogrekci

Dermatopathology 2024, 11(3), 239-252; https://doi.org/10.3390/dermatopathology11030026 - 15 Aug 2024

Cited by 18 | Viewed by 4274

Abstract

Skin tumors, especially melanoma, which is highly aggressive and progresses quickly to other sites, are an issue in various parts of the world. Nevertheless, the one and only way to save lives is to detect it at its initial stages. This study explores the application of advanced deep learning models for classifying benign and malignant melanoma using dermoscopic images. The aim of the study is to enhance the accuracy and efficiency of melanoma diagnosis with the ConvNeXt, Vision Transformer (ViT) Base-16, and Swin Transformer V2 Small (Swin V2 S) deep learning models. The ConvNeXt model, which integrates principles of both convolutional neural networks and transformers, demonstrated superior performance, with balanced precision and recall metrics. The dataset, sourced from Kaggle, comprises 13,900 uniformly sized images, preprocessed to standardize the inputs for the models. Experimental results revealed that ConvNeXt achieved the highest diagnostic accuracy among the tested models. Experimental results revealed that ConvNeXt achieved an accuracy of 91.5%, with balanced precision and recall rates of 90.45% and 92.8% for benign cases, and 92.61% and 90.2% for malignant cases, respectively. The F1-scores for ConvNeXt were 91.61% for benign cases and 91.39% for malignant cases. This research points out the potential of hybrid deep learning architectures in medical image analysis, particularly for early melanoma detection. Full article

(This article belongs to the Section Artificial Intelligence in Dermatopathology)

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11 pages, 444 KB

Open AccessReview

Skin and Syntax: Large Language Models in Dermatopathology

by Asghar Shah, Samer Wahood, Dorra Guermazi, Candice E. Brem and Elie Saliba

Dermatopathology 2024, 11(1), 101-111; https://doi.org/10.3390/dermatopathology11010009 - 14 Feb 2024

Cited by 14 | Viewed by 4123

Abstract

This literature review introduces the integration of Large Language Models (LLMs) in the field of dermatopathology, outlining their potential benefits, challenges, and prospects. It discusses the changing landscape of dermatopathology with the emergence of LLMs. The potential advantages of LLMs include a streamlined generation of pathology reports, the ability to learn and provide up-to-date information, and simplified patient education. Existing instances of LLMs encompass diagnostic support, research acceleration, and trainee education. Challenges involve biases, data privacy and quality, and establishing a balance between AI and dermatopathological expertise. Prospects include the integration of LLMs with other AI technologies to improve diagnostics and the improvement of multimodal LLMs that can handle both text and image input. Our implementation guidelines highlight the importance of model transparency and interpretability, data quality, and continuous oversight. The transformative potential of LLMs in dermatopathology is underscored, with an emphasis on a dynamic collaboration between artificial intelligence (AI) experts (technical specialists) and dermatopathologists (clinicians) for improved patient outcomes. Full article

(This article belongs to the Special Issue Artificial Intelligence in Dermatopathology)

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2 pages, 196 KB

Open AccessEditorial

Artificial Intelligence in Dermatopathology: An Analysis of Its Practical Application

by Marina Kristy Ibraheim, Rohit Gupta, Jerad M. Gardner and Ashley Elsensohn

Dermatopathology 2023, 10(1), 93-94; https://doi.org/10.3390/dermatopathology10010014 - 16 Feb 2023

Cited by 11 | Viewed by 4228

Abstract

In recent years, researchers have explored potential uses for artificial intelligence (AI) in medical practice [...] Full article

(This article belongs to the Special Issue Artificial Intelligence in Dermatopathology)

8 pages, 1172 KB

Open AccessReview

Artificial Intelligence in Dermatopathology: New Insights and Perspectives

by Gerardo Cazzato, Anna Colagrande, Antonietta Cimmino, Francesca Arezzo, Vera Loizzi, Concetta Caporusso, Marco Marangio, Caterina Foti, Paolo Romita, Lucia Lospalluti, Francesco Mazzotta, Sebastiano Cicco, Gennaro Cormio, Teresa Lettini, Leonardo Resta, Angelo Vacca and Giuseppe Ingravallo

Dermatopathology 2021, 8(3), 418-425; https://doi.org/10.3390/dermatopathology8030044 - 1 Sep 2021

Cited by 33 | Viewed by 5668

Abstract

In recent years, an increasing enthusiasm has been observed towards artificial intelligence and machine learning, involving different areas of medicine. Among these, although still in the embryonic stage, the dermatopathological field has also been partially involved, with the attempt to develop and train algorithms that could assist the pathologist in the differential diagnosis of complex melanocytic lesions. In this article, we face this new challenge of the modern era, carry out a review of the literature regarding the state of the art and try to determine promising future perspectives. Full article

(This article belongs to the Section Artificial Intelligence in Dermatopathology)

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