Recognition of human skin diseases using inception-V3 with transfer learning

Mamun, Md. Al; Kabir, Md. Solaiman; Akter, Morium; Uddin, Mohammad Shorif

doi:10.1007/s41870-022-01050-4

Cited by 8 publications

(2 citation statements)

References 31 publications

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“….  Output Layer: After passing through many residual blocks, the final output of the ResNet50 architecture is created by applying a global average pooling operation followed by a fully connected layer with softmax activation [24].…”

Section: B Resnet50mentioning

confidence: 99%

Dermatological Decision Support Systems using CNN for Binary Classification

Dondapati,

Sivaprakasam,

Kumar

2024

Eng. Technol. Appl. Sci. Res.

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Skin cancer diagnosis, particularly melanoma detection, is an important healthcare concern worldwide. This study uses the ISIC2017 dataset to evaluate the performance of three deep learning architectures, VGG16, ResNet50, and InceptionV3, for binary classification of skin lesions as benign or malignant. ResNet50 achieved the highest training-set accuracy of 81.1%, but InceptionV3 outperformed the other classifiers in generalization with a validation accuracy of 76.2%. The findings reveal the various strengths and trade-offs of alternative designs, providing important insights for the development of dermatological decision support systems. This study contributes to the progress of automated skin cancer diagnosis and establishes the framework for future studies aimed at improving classification accuracy.

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Section: B Resnet50mentioning

confidence: 99%

Dermatological Decision Support Systems using CNN for Binary Classification

Dondapati,

Sivaprakasam,

Kumar

2024

Eng. Technol. Appl. Sci. Res.

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“…Many research works have proposed algorithms and methods to diagnose diseases based on clinical signs and symptoms and apply information technology and health. In addition, many applications support and care for patients’ health to improve life expectancy and health care, such as research in [ 5 – 9 ]. Furthermore, Deep Learning (DL) and Machine Learning (ML) approaches are also used in medicine, such as: making clinical diagnoses, assessing disease status, detecting rare diseases, etc.…”

Section: Introductionmentioning

confidence: 99%

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

Nguyen

Pham

et al. 2023

Int. j. inf. tecnol.

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Overcrowding in hospitals in Vietnam has caused many disadvantages in receiving and treating patients. Especially at the stage of receiving and diagnosing procedures taking patients to the treatment departments in the hospital takes up much time. This study proposes a text-based disease diagnosis using text processing techniques (such as Bag of Words, Term Frequency- Inverse Document Frequency, and Tokenizer) combined with classifiers (such as Random Forests (RF), Multi-Layer Perceptron (MLP), Embeddings and Bidirectional Long Short-term memory (LSTM)) on symptoms. As observed from the results, deep Bidirectional LSTM can reach 0.982 in AUC in the classification of 10 diseases on 230,457 samples of pre-diagnosis collected from Vietnam hospitals used in the training and testing phases. The proposed approach is expected to provide a way to automate patient flow in hospitals to improve healthcare in the future.

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SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis

Munjal,

Bhardwaj,

Bhargava

et al. 2024

Health Care Science

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BackgroundSkin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black‐box nature of many models limits interpretability, posing challenges for dermatologists.MethodsTo address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization. A data set of around 50,000 images from the Customized HAM10000, selected for diversity, serves as the foundation. The Inception v3 model is used for classification, supported by gradient‐weighted class activation mapping and local interpretable model‐agnostic explanations algorithms, which provide clear visual explanations for model outputs.ResultsSkinSage XAI demonstrated high performance, accurately categorizing seven types of skin lesions—dermatofibroma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma, and melanoma. It achieved an accuracy of 96%, with precision at 96.42%, recall at 96.28%, F1 score at 96.14%, and an area under the curve of 99.83%.ConclusionsSkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability. The system provides transparent, accurate diagnoses, improving decision‐making for dermatologists and potentially enhancing patient outcomes.

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Recognition of human skin diseases using inception-V3 with transfer learning

Cited by 8 publications

References 31 publications

Dermatological Decision Support Systems using CNN for Binary Classification

Dermatological Decision Support Systems using CNN for Binary Classification

Deep bidirectional LSTM for disease classification supporting hospital admission based on pre-diagnosis: a case study in Vietnam

SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis

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