Skin cancer classification in dermoscopy images using convolutional neural network

Alagu, S.; Bagan, K. Bhoopathy

doi:10.1063/5.0045757

Cited by 7 publications

(2 citation statements)

References 17 publications

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“…They compared the MetaBlock approach with models without using metadata, the baseline concatenation method, and the MetaNet. Alagu and Bagan [ 39 ] utilized the ISIC dataset (500 images) with three classes and achieved an accuracy of 95% using CNN and DenseNet. The study focused on identifying melanoma cells, and the augmented data were trained using DenseNet.…”

Section: Related Workmentioning

confidence: 99%

Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System

Himel,

Islam,

Al-Aff

et al. 2024

International Journal of Biomedical Imaging

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Skin cancer is a significant health concern worldwide, and early and accurate diagnosis plays a crucial role in improving patient outcomes. In recent years, deep learning models have shown remarkable success in various computer vision tasks, including image classification. In this research study, we introduce an approach for skin cancer classification using vision transformer, a state-of-the-art deep learning architecture that has demonstrated exceptional performance in diverse image analysis tasks. The study utilizes the HAM10000 dataset; a publicly available dataset comprising 10,015 skin lesion images classified into two categories: benign (6705 images) and malignant (3310 images). This dataset consists of high-resolution images captured using dermatoscopes and carefully annotated by expert dermatologists. Preprocessing techniques, such as normalization and augmentation, are applied to enhance the robustness and generalization of the model. The vision transformer architecture is adapted to the skin cancer classification task. The model leverages the self-attention mechanism to capture intricate spatial dependencies and long-range dependencies within the images, enabling it to effectively learn relevant features for accurate classification. Segment Anything Model (SAM) is employed to segment the cancerous areas from the images; achieving an IOU of 96.01% and Dice coefficient of 98.14% and then various pretrained models are used for classification using vision transformer architecture. Extensive experiments and evaluations are conducted to assess the performance of our approach. The results demonstrate the superiority of the vision transformer model over traditional deep learning architectures in skin cancer classification in general with some exceptions. Upon experimenting on six different models, ViT-Google, ViT-MAE, ViT-ResNet50, ViT-VAN, ViT-BEiT, and ViT-DiT, we found out that the ML approach achieves 96.15% accuracy using Google’s ViT patch-32 model with a low false negative ratio on the test dataset, showcasing its potential as an effective tool for aiding dermatologists in the diagnosis of skin cancer.

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Section: Related Workmentioning

confidence: 99%

Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System

Himel,

Islam,

Al-Aff

et al. 2024

International Journal of Biomedical Imaging

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“…More often, both melanoma and non-melanoma skin tumors may lead to death of people ( Mühr, Hultin & Dillner, 2021 ). As a result, squamous cell carcinoma and basal cell carcinoma attack non-melanoma skin ( Liu-Smith, Jia & Zheng, 2017 ; Alagu & Bagan, 2021 ). Melanoma causes aberrant melanocyte proliferation, and the cells that produce pigment also give skin its color.…”

Section: Introductionmentioning

confidence: 99%

A novel artificial intelligence-based predictive analytics technique to detect skin cancer

Balaji

Hùng

Chakrabarti

et al. 2023

PeerJ Computer Science

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One of the leading causes of death among people around the world is skin cancer. It is critical to identify and classify skin cancer early to assist patients in taking the right course of action. Additionally, melanoma, one of the main skin cancer illnesses, is curable when detected and treated at an early stage. More than 75% of fatalities worldwide are related to skin cancer. A novel Artificial Golden Eagle-based Random Forest (AGEbRF) is created in this study to predict skin cancer cells at an early stage. Dermoscopic images are used in this instance as the dataset for the system’s training. Additionally, the dermoscopic image information is processed using the established AGEbRF function to identify and segment the skin cancer-affected area. Additionally, this approach is simulated using a Python program, and the current research’s parameters are assessed against those of earlier studies. The results demonstrate that, compared to other models, the new research model produces better accuracy for predicting skin cancer by segmentation.

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Transfer Learning for Automated Melanoma Classification System: Data Augmentation

Yousra

Abdelhakim

Mohamed

2023

Lecture Notes in Networks and Systems

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Skin cancer classification in dermoscopy images using convolutional neural network

Cited by 7 publications

References 17 publications

Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System

Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-Based Noninvasive Digital System

A novel artificial intelligence-based predictive analytics technique to detect skin cancer

Transfer Learning for Automated Melanoma Classification System: Data Augmentation

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