GlaucoViT: A New hybrid vision transformer and EfficientNetB0-based classification model for Glaucoma Diagnosis Using Fundus Images.

📄 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 journal. The current draft lacks proper structure, technical depth, coherence, formatting, and justification. I need it revised or rewritten to match international publishing standards.

Main Requirements/Expectations:

1. All content of papers in literature review section, i.e. literature review should not be like this eg. Author et al [1] did this, then in next paragraph same apprach author2 et al. [5]. Rather every time the paragraph starting should be in a fresh way or style to avoid monotonusness in the paragraphs explaining or talking about every paper cited in the review. This is very imp.2. Introduction section should have atleast 30-35 papers as references for each topic or anything touched there should be a refernce for this and that, authentic references (esp of elsevier, or any good SCI journals, latest also if possible).3. Every where in the paper there should be refernce for every topic discussed, talked about or even slightly touched.4. The graphs are not all or properly explained, for every graph mentioned or every figure included there should be proper justification given, proper references also for them every where possible, no stone should be left unturned. Please add a topic of cross validation also in the paper wherever it fits right and correct and also pls follow an image that i will share as i have developed a graph for it. I will possibly share my code also for the python file if required. If the graphs or results do not look convincing, please do whatever is best.5. There are losts of errors or misssings in the form of table number, figure number, there relevance in the text, etc that needs to be corrected wherever possible. Pls ensure that also wherever possible.6. The paper should follow this overview structure as per my guide for an elsevier level style paper i.e. a) Abstract b) Introduction c) Literature review d) Materials & Methods e)Results and discussions. f)conclusion (since graphs are too many it should be somewhat big) g) references. This is the main overview of structure, in between other sub-topics or sub structures could be or should be added for a phd level professional academic paper.7. Try to add or make as many tables for the data so that the paper looks supremely professional and technical and well written, as many reallistically required. Same for Figures wherever required. 8. Diagrams supporting methodology i.e like in great papers having Overall methodology/ Framework etc and sample images of dataset with image numbers also, proposed model diagrams of excellent quality, etc. 9. Most imp: I have only used one dataset named ACRIMA and not properly explained but as per my guide multiple glaucoma datasets should be comapred and shown as being tested on my model. That should be there. all datasets properly explained with results of model on every on differnt performance metrics in form of great tables and also figures, numbered properly. REFERENCES TO BE INCLUDED EVERYWHERE AS MUCH AS POSSIBLE.10. Performance metrics with proper formula in word document for all metrics. 11. Novel model if required can be molded if required in best interest of this paper.NO PLAGIARISM please AI or other.Kindly give in suitable formats such as word.[Most imp: If you feel like or wish to revamp the whole paper (if its not upto the mark, any level) if and somehow wherever you feel best in interest of the paper to be more suitable as per academic standards than can go ahead just keeping above points in mind.. The paper is not well written or structured and simply looks unprofessional as per my guide(from high impact factor elsevier level SCI journal publication level), above also is his expectations from my paper.]Please make it the best damn paper on Glaucoma classification ever.

✅ 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 and other imp references also.
• Use 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 Elsevier style
• Include recent and highly cited papers.
• 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 elsevier journal-submission ready