A systematic review on colon cancer classification by convolutional neural networks: Architecture, accuracy, and research directions - Scorecard - MDSpire
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A systematic review on colon cancer classification by convolutional neural networks: Architecture, accuracy, and research directions
Clinical Scorecard: A Comprehensive Review of Colon Cancer Classification Utilizing Convolutional Neural Networks: Framework, Precision, and Future Research Avenues
At a Glance
Category
Detail
Condition
Colon Cancer
Key Mechanisms
Convolutional Neural Networks (CNNs) for image processing and classification tasks.
Target Population
Patients undergoing colon cancer diagnosis via histopathological image analysis.
Care Setting
Medical imaging and image processing in oncology.
Key Highlights
CNNs improve accuracy in colon cancer diagnosis compared to traditional methods.
Recent studies focus on bi-classification and multi-classification tasks in colon cancer.
Only 0.7% of CNN studies specifically address colon cancer classification.
The review emphasizes the need for systematic comparisons of CNN performance.
High accuracy (≥85%) is a criterion for included studies in the review.
Guideline-Based Recommendations
Diagnosis
Utilize CNN models for enhanced accuracy in histopathological image classification.
Management
Implement automated tools based on CNNs to streamline the diagnosis process.
Monitoring & Follow-up
Assess the performance of CNN models using established accuracy metrics.
Risks
Potential biases in study selection and methodological quality must be evaluated.
Patient & Prescribing Data
Individuals diagnosed with colon cancer requiring histopathological evaluation.
AI-driven tools can facilitate more reliable diagnosis and classification.
Clinical Best Practices
Adopt the PICOS framework for systematic review processes.
Ensure transparency and reproducibility in research by using open-access data.
Evaluate methodological quality using the PROBAST tool.
