⚡ Quick Summary
This study explored the use of pre-trained deep learning models for the detection of oral squamous cell carcinoma (OSCC), a cancer type responsible for 364,339 deaths in 2020. The research demonstrated a remarkable accuracy of 90% and a sensitivity of 97%, highlighting the potential of these technologies in enhancing early cancer diagnosis.
🔍 Key Details
- 📊 Dataset: 5,192 histopathological images
- 🧩 Features used: Histopathological images for OSCC classification
- ⚙️ Technology: Pre-trained models including ResNet-50, VGG16, and InceptionV3
- 🏆 Performance: Highest model achieved accuracy of 0.90, sensitivity of 0.97, and AUC of 0.94
🔑 Key Takeaways
- 📊 Early detection of OSCC significantly improves prognosis.
- 💡 Transfer learning methodologies can enhance diagnostic accuracy in medical imaging.
- 👩🔬 The study utilized a dataset of 5,192 histopathological images for training and testing.
- 🏆 The best-performing model achieved an accuracy of 90%.
- 🤖 Sensitivity of 97% indicates high reliability in detecting OSCC.
- 🌍 The study suggests practical applications for these models in clinical settings.
- 🔮 Future research is encouraged to further improve diagnostic precision.
📚 Background
Oral squamous cell carcinoma (OSCC) is the 13th most common cancer globally, with a significant mortality rate. Early detection is crucial, as it correlates strongly with improved survival rates. Traditional diagnostic methods, such as tissue biopsy, are often costly and time-consuming, prompting the need for innovative approaches that can expedite diagnosis and improve patient outcomes.
🗒️ Study
The study aimed to evaluate the effectiveness of transfer learning using pre-trained deep learning models for the binary classification of OSCC from histopathological images. Researchers employed models like ResNet-50, VGG16, and InceptionV3, alongside a tuned convolutional neural network (CNN), to analyze a comprehensive dataset of 5,192 images.
📈 Results
The results were promising, with the highest-performing model achieving an accuracy of 90%, a sensitivity of 97%, and an AUC of 0.94. These metrics were visualized using ROC curves and confusion matrices, providing a clear representation of the model’s performance. The study emphasizes the importance of sensitivity in medical diagnostics, particularly for cancer detection.
🌍 Impact and Implications
The findings of this study could significantly impact the field of oncology by providing a reliable tool for early diagnosis of OSCC. By integrating advanced deep learning techniques into clinical practice, healthcare professionals can enhance diagnostic precision, ultimately leading to better patient outcomes and potentially reducing mortality rates associated with this aggressive cancer type.
🔮 Conclusion
This research highlights the transformative potential of deep learning in the medical field, particularly for cancer diagnosis. The successful application of transfer learning methodologies demonstrates a promising avenue for future research aimed at improving diagnostic tools. As technology continues to evolve, we can anticipate even greater advancements in the early detection and treatment of cancers like OSCC.
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DETECTION OF ORAL SQUAMOUS CELL CARCINOMA USING PRE-TRAINED DEEP LEARNING MODELS.
Abstract
BACKGROUND: Oral squamous cell carcinoma (OSCC), the 13th most common type of cancer, claimed 364,339 lives in 2020. Researchers have established a strong correlation between early detection and better prognosis for this type of cancer. Tissue biopsy, the most common diagnostic method used by doctors, is both expensive and time-consuming. The recent growth in using transfer learning methodologies to aid in medical diagnosis, along with the improved 5-year survival rate from early diagnosis serve as motivation for this study. The aim of the study was to evaluate an innovative approach using transfer learning of pre-trained classification models and convolutional neural networks (CNN) for the binary classification of OSCC from histopathological images.
MATERIALS AND METHODS: The dataset used for the experiments consisted of 5192 histopathological images in total. The following pre-trained deep learning models were used for feature extraction: ResNet-50, VGG16, and InceptionV3 along with a tuned CNN for classification.
RESULTS: The proposed methodologies were evaluated against the current state of the art. A high sensitivity and its importance in the medical field were highlighted. All three models were used in experiments with different hyperparameters and tested on a set of 126 histopathological images. The highest-performance developed model achieved an accuracy of 0.90, a sensitivity of 0.97, and an AUC of 0.94. The visualization of the results was done using ROC curves and confusion matrices. The study further interprets the results obtained and concludes with suggestions for future research.
CONCLUSION: The study successfully demonstrated the potential of using transfer learning-based methodologies in the medical field. The interpretation of the results suggests their practical viability and offers directions for future research aimed at improving diagnostic precision and serving as a reliable tool to physicians in the early diagnosis of cancer.
Author: [‘Dhanya K’, ‘Prasad DVV’, ‘Lokeswari YV’]
Journal: Exp Oncol
Citation: Dhanya K, et al. DETECTION OF ORAL SQUAMOUS CELL CARCINOMA USING PRE-TRAINED DEEP LEARNING MODELS. DETECTION OF ORAL SQUAMOUS CELL CARCINOMA USING PRE-TRAINED DEEP LEARNING MODELS. 2024; 46:119-128. doi: 10.15407/exp-oncology.2024.02.119