Sajid Khan, Muhammad Asif Khan, Adeeb Noor, Kainat Fareed
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This allows models to concentrate on critical areas within the images while ignoring learning the noisy features.</p><p><strong>Results: </strong>Various deep learning segmentation models such as UNet, LinkNet, PSPNet, and FPN were trained on the SASAN dataset to perform segmentation-based ROI extraction. Classification was then performed using the dataset with and without ROI extraction. The results demonstrate that ROI extraction significantly improves the performance of these models in classification. This implies that SASAN is effective in evaluating performance metrics for complex skin cancer cases.</p><p><strong>Conclusions: </strong>This study highlights the importance of expanding datasets to include challenging scenarios and developing better segmentation methods to enhance automated skin cancer diagnosis. The SASAN dataset serves as a valuable tool for researchers aiming to improve such systems and ultimately contribute to better diagnostic outcomes.</p>","PeriodicalId":11273,"journal":{"name":"Diagnosis","volume":" ","pages":"283-294"},"PeriodicalIF":2.2000,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"SASAN: ground truth for the effective segmentation and classification of skin cancer using biopsy images.\",\"authors\":\"Sajid Khan, Muhammad Asif Khan, Adeeb Noor, Kainat Fareed\",\"doi\":\"10.1515/dx-2024-0012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>Early skin cancer diagnosis can save lives; however, traditional methods rely on expert knowledge and can be time-consuming. This calls for automated systems using machine learning and deep learning. However, existing datasets often focus on flat skin surfaces, neglecting more complex cases on organs or with nearby lesions.</p><p><strong>Methods: </strong>This work addresses this gap by proposing a skin cancer diagnosis methodology using a dataset named ASAN that covers diverse skin cancer cases but suffers from noisy features. To overcome the noisy feature problem, a segmentation dataset named SASAN is introduced, focusing on Region of Interest (ROI) extraction-based classification. This allows models to concentrate on critical areas within the images while ignoring learning the noisy features.</p><p><strong>Results: </strong>Various deep learning segmentation models such as UNet, LinkNet, PSPNet, and FPN were trained on the SASAN dataset to perform segmentation-based ROI extraction. Classification was then performed using the dataset with and without ROI extraction. The results demonstrate that ROI extraction significantly improves the performance of these models in classification. This implies that SASAN is effective in evaluating performance metrics for complex skin cancer cases.</p><p><strong>Conclusions: </strong>This study highlights the importance of expanding datasets to include challenging scenarios and developing better segmentation methods to enhance automated skin cancer diagnosis. 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引用次数: 0
摘要
目的:皮肤癌的早期诊断可以挽救生命;然而,传统方法依赖于专家知识,可能非常耗时。这就需要使用机器学习和深度学习的自动化系统。然而,现有的数据集往往侧重于平坦的皮肤表面,而忽略了器官上或附近病变的更复杂病例:该数据集涵盖了各种皮肤癌病例,但存在噪声特征问题。为了克服噪声特征问题,我们引入了名为 SASAN 的分割数据集,重点关注基于兴趣区域(ROI)提取的分类。这使得模型能够专注于图像中的关键区域,同时忽略噪声特征的学习:在 SASAN 数据集上训练了各种深度学习分割模型,如 UNet、LinkNet、PSPNet 和 FPN,以执行基于分割的 ROI 提取。然后使用有无 ROI 提取的数据集进行分类。结果表明,ROI 提取大大提高了这些模型的分类性能。这意味着 SASAN 可以有效评估复杂皮肤癌病例的性能指标:本研究强调了扩展数据集以包括具有挑战性的场景和开发更好的分割方法以提高皮肤癌自动诊断能力的重要性。SASAN 数据集是研究人员改进此类系统的宝贵工具,最终有助于提高诊断结果。
SASAN: ground truth for the effective segmentation and classification of skin cancer using biopsy images.
Objectives: Early skin cancer diagnosis can save lives; however, traditional methods rely on expert knowledge and can be time-consuming. This calls for automated systems using machine learning and deep learning. However, existing datasets often focus on flat skin surfaces, neglecting more complex cases on organs or with nearby lesions.
Methods: This work addresses this gap by proposing a skin cancer diagnosis methodology using a dataset named ASAN that covers diverse skin cancer cases but suffers from noisy features. To overcome the noisy feature problem, a segmentation dataset named SASAN is introduced, focusing on Region of Interest (ROI) extraction-based classification. This allows models to concentrate on critical areas within the images while ignoring learning the noisy features.
Results: Various deep learning segmentation models such as UNet, LinkNet, PSPNet, and FPN were trained on the SASAN dataset to perform segmentation-based ROI extraction. Classification was then performed using the dataset with and without ROI extraction. The results demonstrate that ROI extraction significantly improves the performance of these models in classification. This implies that SASAN is effective in evaluating performance metrics for complex skin cancer cases.
Conclusions: This study highlights the importance of expanding datasets to include challenging scenarios and developing better segmentation methods to enhance automated skin cancer diagnosis. The SASAN dataset serves as a valuable tool for researchers aiming to improve such systems and ultimately contribute to better diagnostic outcomes.
期刊介绍:
Diagnosis focuses on how diagnosis can be advanced, how it is taught, and how and why it can fail, leading to diagnostic errors. The journal welcomes both fundamental and applied works, improvement initiatives, opinions, and debates to encourage new thinking on improving this critical aspect of healthcare quality. Topics: -Factors that promote diagnostic quality and safety -Clinical reasoning -Diagnostic errors in medicine -The factors that contribute to diagnostic error: human factors, cognitive issues, and system-related breakdowns -Improving the value of diagnosis – eliminating waste and unnecessary testing -How culture and removing blame promote awareness of diagnostic errors -Training and education related to clinical reasoning and diagnostic skills -Advances in laboratory testing and imaging that improve diagnostic capability -Local, national and international initiatives to reduce diagnostic error