Uncertainty Quantification to Improve the Classification of Melanoma and Basal Skin Cancer Using ResNet Model

Deepa, D.; Muthukumaran, V.; Vinodhini, V.; Selvaraj, Senthil; Kumar, M. Sandeep; Jayagopal, Prabhu

doi:10.1142/s1752890922420107

Cited by 3 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MC dropout technique adeptly identified out-of-domain data, reducing training time. Consequently, the ResNet50 model achieved an impressive 89% accuracy in skin cancer classification [40].…”

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Jaganathan,

Balasubramaniam,

Sureshkumar

et al. 2024

Diagnostics

View full text Add to dashboard Cite

Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121—each Convolutional Neural Network architecture designed for classification tasks—this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Jaganathan,

Balasubramaniam,

Sureshkumar

et al. 2024

Diagnostics

View full text Add to dashboard Cite

show abstract

“…They also incorporated transfer learning, and their results showed that the proposed model achieved an accuracy of 96.5%, 98%, and 89% for the HAM10000, ISBI2018, and ISIC2019 datasets, respectively. In another study, Deepa et al [36] trained the ResNet50 model on the International Skin Image Collaboration (ISIC) dataset and achieved 89% accuracy. Tahir et al [37] proposed a deep learning model called DSCC_Net, trained that on three datasets, ISIC 2020, HAM10000, and DermIS, and achieved an accuracy of 99%.…”

Section: Introductionmentioning

confidence: 99%

SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions

Mehmood

Gulzar

Ilyas

et al. 2023

Cancers

View full text Add to dashboard Cite

Skin cancer is a major public health concern around the world. Skin cancer identification is critical for effective treatment and improved results. Deep learning models have shown considerable promise in assisting dermatologists in skin cancer diagnosis. This study proposes SBXception: a shallower and broader variant of the Xception network. It uses Xception as the base model for skin cancer classification and increases its performance by reducing the depth and expanding the breadth of the architecture. We used the HAM10000 dataset, which contains 10,015 dermatoscopic images of skin lesions classified into seven categories, for training and testing the proposed model. Using the HAM10000 dataset, we fine-tuned the new model and reached an accuracy of 96.97% on a holdout test set. SBXception also achieved significant performance enhancement with 54.27% fewer training parameters and reduced training time compared to the base model. Our findings show that reducing and expanding the Xception model architecture can greatly improve its performance in skin cancer categorization.

show abstract

Melanoma detection using Egret search golden optimization – Deep convolutional neural network model

Fatima,

Akther

2024

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

Uncertainty Quantification to Improve the Classification of Melanoma and Basal Skin Cancer Using ResNet Model

Cited by 3 publications

References 27 publications

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions

Melanoma detection using Egret search golden optimization – Deep convolutional neural network model

Contact Info

Product

Resources

About