Big transfer learning for automated skin cancer classification

Arkah, Zinah Mohsin; Al-Dulaimi, Dalya S.; Khekan, Ahlam R.

doi:10.11591/ijeecs.v23.i3.pp1611-1619

Cited by 6 publications

(5 citation statements)

References 44 publications

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“…Table 8 shows that the proposed approach ranked second after L. Alzubaidi et al (2021) [ 10 ], where they obtained an accuracy of 98.57%, a precision of 99.18%, a recall of 97.90%, and an F1 score of 98.53%. Z. M. Arkah (2021) [ 18 ] ranked third with an accuracy of 93.7%, a precision of 95.7%, a recall of 94.6%, and an F1-score of 95.1%. V. Shah et al (2020) [ 15 ] came in fourth, with an accuracy of 93.96%, a precision of 94.11%, a recall of 93.96%, an F1-score of 93.24%, a sensitivity of 99.7%, and a specificity of 55.67%.…”

Section: Resultsmentioning

confidence: 99%

“…His model suffered from an overfitting problem. Z. M. Arkah et al, 2021 [ 18 ], proposed a new approach to transfer learning by training the models (VGG, GoogleNet, ResNet50) from scratch on a large number of unlabeled melanoma images, and then training them on a small number of labeled skin images. They applied their approach to the ISIC 2020 dataset.…”

Section: Introductionmentioning

confidence: 99%

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Incorporating a Novel Dual Transfer Learning Approach for Medical Images

Mukhlif

Al‐Khateeb

Mohammed

2023

Sensors

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Recently, transfer learning approaches appeared to reduce the need for many classified medical images. However, these approaches still contain some limitations due to the mismatch of the domain between the source domain and the target domain. Therefore, this study aims to propose a novel approach, called Dual Transfer Learning (DTL), based on the convergence of patterns between the source and target domains. The proposed approach is applied to four pre-trained models (VGG16, Xception, ResNet50, MobileNetV2) using two datasets: ISIC2020 skin cancer images and ICIAR2018 breast cancer images, by fine-tuning the last layers on a sufficient number of unclassified images of the same disease and on a small number of classified images of the target task, in addition to using data augmentation techniques to balance classes and to increase the number of samples. According to the obtained results, it has been experimentally proven that the proposed approach has improved the performance of all models, where without data augmentation, the performance of the VGG16 model, Xception model, ResNet50 model, and MobileNetV2 model are improved by 0.28%, 10.96%, 15.73%, and 10.4%, respectively, while, with data augmentation, the VGG16 model, Xception model, ResNet50 model, and MobileNetV2 model are improved by 19.66%, 34.76%, 31.76%, and 33.03%, respectively. The Xception model obtained the highest performance compared to the rest of the models when classifying skin cancer images in the ISIC2020 dataset, as it obtained 96.83%, 96.919%, 96.826%, 96.825%, 99.07%, and 94.58% for accuracy, precision, recall, F1-score, sensitivity, and specificity respectively. To classify the images of the ICIAR 2018 dataset for breast cancer, the Xception model obtained 99%, 99.003%, 98.995%, 99%, 98.55%, and 99.14% for accuracy, precision, recall, F1-score, sensitivity, and specificity, respectively. Through these results, the proposed approach improved the models’ performance when fine-tuning was performed on unclassified images of the same disease.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incorporating a Novel Dual Transfer Learning Approach for Medical Images

Mukhlif

Al‐Khateeb

Mohammed

2023

Sensors

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“…− True negative (TN) is a negative object class and negative image position prediction. Furthermore, the following metrics are calculated using the confusion matrix terminology below [4], [34].…”

Section: Evaluation Methodsmentioning

confidence: 99%

Two-stages of segmentation to improve accuracy of deep learning models based on dairy cow morphology

Siregar¹,

Purwanto²,

Wijaya³

et al. 2023

IJECE

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<span lang="EN-US">Computer vision deals with image-based problems, such as deep learning, classification, and object detection. This study classifies the quality of dairy parents into three, namely high, medium, and low based on morphology by focusing on Bogor Indonesia farms. The morphological images used are the side and back of dairy cows and the challenge is to determine the optimal accuracy of the model for it to be implemented into an automated system. The 2-step mask region-based convolutional neural network (mask R-CNN) and Canny segmentation algorithm were continuously used to classify the convolutional neural network (CNN) in order to obtain optimal accuracy. When testing the model using training and testing ratios of 90:10 and 80:20, it was evaluated in terms of accuracy, precision, recall, and F1-score. The results showed that the highest model produced an accuracy of 85.44%, 87.12% precision, 83.79% recall, and 84.94% F1-score. Therefore, it was concluded that the test result with 2-stages of segmentation was the best.</span>

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“…In contrast, DNNs are state-of-the-art image analysis tools that perform exceptionally well in image classification tasks [8]. DNNs, which consist of multiple layers of interconnected artificial neurons, are adept at deriving intricate image features and making accurate predictions [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques

Hamida,

Lamrani,

El Gannour

et al. 2024

Bulletin EEI

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Significant health concerns are associated with skin diseases, and accurate and timely diagnosis is essential for effective treatment and patient management. To improve the classification of cutaneous diseases, we propose a novel hybrid system that incorporates the strengths of random forest (RF) and deep neural network (DNN) algorithms. The system employs data augmentation and balancing techniques to enhance model performance and generalizability. The HAM10000 dataset of diverse dermatoscopic images is used for training and evaluation in this study. In the hybrid system proposed, the RF model provides an initial diagnosis based on patient-reported symptoms, while the DNN analyzes images of skin lesions, resulting in more precise and efficient diagnoses. Using hyper-parameter optimization, we fine-tune the system for optimal performance. The evaluation demonstrates the accuracy of the hybrid model, which achieves a classification accuracy of 96.8% overall. According to our findings, the hybrid system demonstrates exceptional efficacy in six of seven skin disease classes. Variations in sensitivity and reliance on data quality and quantity are however cited as limitations. Nevertheless, this hybrid system has the potential to revolutionize skin disease diagnosis and treatment.

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Big transfer learning for automated skin cancer classification

Cited by 6 publications

References 44 publications

Incorporating a Novel Dual Transfer Learning Approach for Medical Images

Incorporating a Novel Dual Transfer Learning Approach for Medical Images

Two-stages of segmentation to improve accuracy of deep learning models based on dairy cow morphology

Toward enhanced skin disease classification using a hybrid RF-DNN system leveraging data balancing and augmentation techniques

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