Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis
Abstract Major underlying health issues can be indicated by even minor nail infections. Subungual Melanoma is one of the most severe kinds since it is identified at a much later stage than other conditions. The purpose of this research is to offer novel deep-learning algorithms that target the auton...
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| Format: | Article |
| Language: | English |
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BMC
2024-12-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02840-5 |
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| author | Gunjan Shandilya Sheifali Gupta Salil Bharany Ateeq Ur Rehman Upinder Kaur Hafizan Mat Som Seada Hussen |
| author_facet | Gunjan Shandilya Sheifali Gupta Salil Bharany Ateeq Ur Rehman Upinder Kaur Hafizan Mat Som Seada Hussen |
| author_sort | Gunjan Shandilya |
| collection | DOAJ |
| description | Abstract Major underlying health issues can be indicated by even minor nail infections. Subungual Melanoma is one of the most severe kinds since it is identified at a much later stage than other conditions. The purpose of this research is to offer novel deep-learning algorithms that target the autonomous categorization of six forms of nail disorders by employing images: Blue Finger, Clubbing, Pitting, Onychogryphosis, Acral Lentiginous Melanoma, and Normal Nail or Healthy Nail Appearance. Based on this, we build an initial baseline CNN model, which is then further advanced by the introduction of the Hybrid Capsule CNN model by the reduction of space hierarchy deficiencies of the classic CNN model. All these models were trained and tested using the Nail Disease Detection dataset with intensive uses of techniques of data augmentation. The Hybrid Capsule CNN model, thus, provided superior classification accuracy compared to the others; the training accuracy was 99.40%, while the validation accuracy was 99.25%, whereas the hybrid model outperformed the Base CNN model with astounding precision, recall of 97.35% and 96.79%. The hybrid model additionally leverages the capsule network and dynamic routing, offering improved robustness against transformations as well as improving spatial properties. The current study consequently provides a very viable, economical, and accessible diagnostic tool, especially for places with a paucity of medical services. The proposed methodology provides tremendous capacity for early diagnosis and better outcomes for the patient in a healthcare scenario. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-66f4211fba7a48dea8256f7d803159db |
| institution | DOAJ |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-66f4211fba7a48dea8256f7d803159db2025-08-20T02:46:12ZengBMCBMC Medical Informatics and Decision Making1472-69472024-12-0124111910.1186/s12911-024-02840-5Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosisGunjan Shandilya0Sheifali Gupta1Salil Bharany2Ateeq Ur Rehman3Upinder Kaur4Hafizan Mat Som5Seada Hussen6Chitkara University Institute of Engineering and Technology, Chitkara UniversityChitkara University Institute of Engineering and Technology, Chitkara UniversityChitkara University Institute of Engineering and Technology, Chitkara UniversitySchool of Computing, Gachon UniversityDepartment of Computer Science and Engineering, Lovely Professional UniversityComputer and Information Sciences Department, Faculty of Science and Information Technology, Universiti Teknologi PetronasDepartment of Electrical Power, Adama Science and Technology UniversityAbstract Major underlying health issues can be indicated by even minor nail infections. Subungual Melanoma is one of the most severe kinds since it is identified at a much later stage than other conditions. The purpose of this research is to offer novel deep-learning algorithms that target the autonomous categorization of six forms of nail disorders by employing images: Blue Finger, Clubbing, Pitting, Onychogryphosis, Acral Lentiginous Melanoma, and Normal Nail or Healthy Nail Appearance. Based on this, we build an initial baseline CNN model, which is then further advanced by the introduction of the Hybrid Capsule CNN model by the reduction of space hierarchy deficiencies of the classic CNN model. All these models were trained and tested using the Nail Disease Detection dataset with intensive uses of techniques of data augmentation. The Hybrid Capsule CNN model, thus, provided superior classification accuracy compared to the others; the training accuracy was 99.40%, while the validation accuracy was 99.25%, whereas the hybrid model outperformed the Base CNN model with astounding precision, recall of 97.35% and 96.79%. The hybrid model additionally leverages the capsule network and dynamic routing, offering improved robustness against transformations as well as improving spatial properties. The current study consequently provides a very viable, economical, and accessible diagnostic tool, especially for places with a paucity of medical services. The proposed methodology provides tremendous capacity for early diagnosis and better outcomes for the patient in a healthcare scenario. Clinical trial number Not applicable.https://doi.org/10.1186/s12911-024-02840-5Convolutional neural network (CNN)Nail classificationDeep learning (D.L)Nail diseasesHybrid modelCapsule network (CapsNet) |
| spellingShingle | Gunjan Shandilya Sheifali Gupta Salil Bharany Ateeq Ur Rehman Upinder Kaur Hafizan Mat Som Seada Hussen Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis BMC Medical Informatics and Decision Making Convolutional neural network (CNN) Nail classification Deep learning (D.L) Nail diseases Hybrid model Capsule network (CapsNet) |
| title | Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis |
| title_full | Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis |
| title_fullStr | Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis |
| title_full_unstemmed | Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis |
| title_short | Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis |
| title_sort | autonomous detection of nail disorders using a hybrid capsule cnn a novel deep learning approach for early diagnosis |
| topic | Convolutional neural network (CNN) Nail classification Deep learning (D.L) Nail diseases Hybrid model Capsule network (CapsNet) |
| url | https://doi.org/10.1186/s12911-024-02840-5 |
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