An Ensemble Voting Method of Pre-Trained Deep Learning Models for Skin Disease Identification

Trang, Kien; Nguyen, Hoang An; TonThat, Long; Ngoc, Hung; Vuong, Bao Quoc

doi:10.1109/cyberneticscom55287.2022.9865634

Cited by 4 publications

(2 citation statements)

References 16 publications

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“…On the other hand, ML is greatly contributing to anomaly detection in various fields, including health care, vehicular networks [19,20], the Internet of Things (IoT), E-commerce, and so on. ML and DL algorithms significantly aid in identifying skin cancer, with early detection potentially leading to successful treatment, making melanoma a significant health concern [21][22][23][24]. Various ML and DL techniques have been applied in existing literature, such as in [25], where the authors presented a convolutional neural network (CNN) based DL stacked ensemble framework for melanoma skin cancer detection using transfer learning.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Skin Cancer Survival Prediction with Ensemble Techniques

Abbasi,

Deng,

Magsi

et al. 2023

Bioengineering

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The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan–Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods. Our proposed ML-based ensemble techniques are applied to a publicly available dataset from the ICGC Data Portal, specifically targeting skin cutaneous melanoma cancers (SKCM). We used eight baseline classifiers, namely, random forest (RF), decision tree (DT), gradient boosting (GB), AdaBoost, Gaussian naïve Bayes (GNB), extra tree (ET), logistic regression (LR), and light gradient boosting machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on SKCM. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of accuracy compared to traditional methods. Specifically, the RF classifier exhibited outstanding precision results. Additionally, four different ensemble methods (stacking, bagging, boosting, and voting) were created and trained to achieve optimal results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves, where the voting method achieved a promising accuracy of 99%. On the other hand, the RF classifier achieved an outstanding accuracy of 99%, which exhibits the best performance. We compared our proposed study with the existing state-of-the-art techniques and found significant improvements in several key aspects. Our approach not only demonstrated superior performance in terms of accuracy but also showcased remarkable efficiency. Thus, this research work contributes to diagnosing SKCM with high accuracy.

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Section: Related Workmentioning

confidence: 99%

Optimizing Skin Cancer Survival Prediction with Ensemble Techniques

Abbasi,

Deng,

Magsi

et al. 2023

Bioengineering

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“…This model efficiently learns to focus on target structures of varying shapes and sizes within the dataset, maintaining prediction accuracy without significant energy costs. In addition to these segmentation-focused studies, others, documented in references [13], have developed neural network models specifically for monitoring oil spills, while [14,15] delve into the development of neural network models for healthcare-related applications.…”

Section: Introductionmentioning

confidence: 99%

OptimalNN: A Neural Network Architecture to Monitor Chemical Contamination in Cancer Alley

Udeji,

Margala

2024

JLPEA

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The detrimental impact of toxic chemicals, gas, and oil spills in aquatic environments poses a severe threat to plants, animals, and human life. Regions such as Cancer Alley exemplify the profound consequences of inadequately controlled chemical spills, significantly affecting the local community. Given the far-reaching effects of these spills, it has become imperative to devise an efficient method for early monitoring, estimation, and cleanup, utilizing affordable and effective techniques. In this research, we explore the application of U-shaped neural Network (UNET) and U-shaped neural network transformer (UNETR) neural network models designed for the image segmentation of chemical and oil spills. Our models undergo training using the Commonwealth Scientific and Industrial Research Organization (CSIRO) dataset and the Oil Spill Detection dataset, employing a specialized filtering technique to enhance detection accuracy. We achieved training accuracies of 95.35% and 91% by applying UNET on the Oil Spill and the CSIRO datasets after 50 epochs of training, respectively. We also achieved a training accuracy of 75% by applying UNETR to the Oil Spill dataset. Additionally, we integrated mixed precision to expedite the model training process, thus maximizing data throughput. To further accelerate our implementation, we propose the utilization of the Field Programmable Gate Array (FPGA) architecture. The results obtained from our study demonstrate improvements in inference latency on FPGA.

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