📄 Assignment Instructions for EssayPro – Research Paper on Vision Transformers for Glaucoma Classification
🧠 Objective:
I am writing a research paper that proposes a novel Vision Transformer (ViT)–based model for Glaucoma classification. This paper is intended for submission to a high-impact Elsevier/IEEE/Springer journal. The current draft lacks proper structure, technical depth, coherence, formatting, and justification. I need it revised or rewritten to match international publishing standards.
✅ Final Output Requirements
- Well-structured scientific paper with clear logical flow
- Language must be formal, technical, and concise
- Free from grammatical errors, repetition, and fluff
- Justifications for all experimental choices must be backed by latest research references (2021–2025)
- Must strictly follow a standard scientific paper template (detailed below)
- Use IEEE or Elsevier reference formatting consistently
📘 Required Paper Structure
1. Title Page
- Concise, technical title reflecting ViT and Glaucoma
- Authors (placeholder names)
- Abstract (150–250 words): standalone summary
- 4–6 Keywords
2. Introduction
- State the problem: importance of Glaucoma detection
- Limitations of existing methods (e.g., CNN)
- What this paper proposes (Vision Transformer–based model)
- Clear contributions in bullet/paragraph form
- End with a short outline of the paper structure
3. Literature Review
- Prior work on Glaucoma classification using ML/DL
- Specific comparison of CNNs vs Transformers
- Identification of research gaps
- Include 5–10 latest references (from 2021–2025)
4. Materials and Methods
- Dataset name, origin, and description
- Explain why this dataset was chosen (with citations)
- Justify train-validation-test split (e.g., 80-10-10) with reference or empirical reason
- Preprocessing steps and data augmentation
- Architecture of the Vision Transformer (layers, modules, modifications if any)
- Training configuration: optimizer, loss, epochs, batch size, hardware used
5. Results and Discussion
- Report evaluation metrics: accuracy, AUC, sensitivity, specificity, F1-score
- Use tables and figures with proper captions and numbering
- Compare results with existing models
- Include confusion matrix, ROC curve, or attention map visualizations
- Discuss outcomes and what they imply
6. Conclusion
- Summarize findings
- Highlight limitations
- Suggest future work
7. References
- Use APA, IEEE, or Elsevier style
- Include recent and highly cited papers (2021–2025)
- Include citations for dataset use, model architecture, training strategy, and evaluation methodology
📌 Additional Notes
- Coherence between sections is important; transitions should be smooth
- Avoid redundancy, keep it concise but informative
- Do not skip dataset justification – explain why this specific dataset is appropriate
- Each table/figure must be referenced in the text
- All methodology choices (splits, hyperparameters) should be justified with references or logic
- Final document should be cleanly formatted and journal-submission ready