QUANTIFYING EXPLAINABLE AI METHODS IN MEDICAL DIAGNOSIS: A STUDY IN SKIN CANCER DOI Creative Commons

Hardik Sangwan

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 10, 2024

Abstract Deep learning models have shown substantial promise in assisting medical diagnosis, offering the potential to improve patient outcomes and reduce clinician workloads. However, widespread adoption of these clinical practice has been hindered by concerns surrounding their trustworthiness, transparency, interpretability. Addressing challenges requires not only development explainable AI (xAI) techniques but also quantitative metrics evaluate effectiveness. This study presents a comprehensive framework for training, explaining, quantitatively assessing deep skin cancer diagnosis. Leveraging HAM10000 dataset seven diagnostic lesion categories, multiple convolutional neural network architectures—including custom CNNs, DenseNet, MobileNet, ResNet—were trained optimized using augmentation, oversampling, hyperparameter tuning. Following model explainability such as SHAP, LIME, Integrated Gradients were deployed generate post hoc explanations. Critically, primary contribution this work is evaluation explanation methods related faithfulness, robustness, complexity. All code, models, results are publicly available, providing reproducible pathway toward more trustworthy, tools.

Язык: Английский

DSCIMABNet: A novel multi-head attention depthwise separable CNN model for skin cancer detection DOI
Hatice Çatal Reis, Veysel Turk

Pattern Recognition, Год журнала: 2024, Номер 159, С. 111182 - 111182

Опубликована: Ноя. 7, 2024

Язык: Английский

Процитировано

1
Skin Disease Recognition Based on Deep Learning Algorithms: A Review DOI Creative Commons

Ahwaz Darweesh,

Adnan Mohsin

Indonesian Journal of Computer Science, Год журнала: 2024, Номер 13(3)

Опубликована: Июнь 15, 2024

The sharp increase in cases of melanoma and other skin cancers worldwide highlights the urgent need for improved diagnostic methods. Because lesions vary widely access to dermatological knowledge is limited resource-poor areas, traditional methods - which rely on visual inspection clinical experience have difficulty identifying diseases accurately. This situation requires innovative approaches improve accessibility accuracy. To address these issues, this work uses deep learning (DL) convolutional neural networks (CNNs). paper trying transform cancer diagnosis through use large databases dermoscopic images advanced artificial intelligence algorithms. In order evaluate effectiveness CNNs DL diseases, we conducted a comprehensive analysis literature, focusing accuracy type classification. Our approach focused model architectures, data preparation methods, performance indicators while examining existing research using AI algorithms diagnose cancer. With ultimate goal improving patient outcomes early detection accurate classification conditions, not only underscores great potential CNN transcending limitations, but also continued development AI-based tools pathology. Dermatology. Diagnosis.

Язык: Английский

Процитировано

0
Can Vision Transformers Be the Next State-of-the-Art Model for Oncology Medical Image Analysis? DOI
S. Venugopal

AI in Precision Oncology, Год журнала: 2024, Номер 1(6), С. 286 - 305

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

0
QUANTIFYING EXPLAINABLE AI METHODS IN MEDICAL DIAGNOSIS: A STUDY IN SKIN CANCER DOI Creative Commons

Hardik Sangwan

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Дек. 10, 2024

Abstract Deep learning models have shown substantial promise in assisting medical diagnosis, offering the potential to improve patient outcomes and reduce clinician workloads. However, widespread adoption of these clinical practice has been hindered by concerns surrounding their trustworthiness, transparency, interpretability. Addressing challenges requires not only development explainable AI (xAI) techniques but also quantitative metrics evaluate effectiveness. This study presents a comprehensive framework for training, explaining, quantitatively assessing deep skin cancer diagnosis. Leveraging HAM10000 dataset seven diagnostic lesion categories, multiple convolutional neural network architectures—including custom CNNs, DenseNet, MobileNet, ResNet—were trained optimized using augmentation, oversampling, hyperparameter tuning. Following model explainability such as SHAP, LIME, Integrated Gradients were deployed generate post hoc explanations. Critically, primary contribution this work is evaluation explanation methods related faithfulness, robustness, complexity. All code, models, results are publicly available, providing reproducible pathway toward more trustworthy, tools.

Язык: Английский

Процитировано

0