Work & project overview

Work activity

Studied the fundus-classification inference pipeline from image preprocessing through prediction display.

Learning / output

Reviewed 224×224 preprocessing, feature extraction, the 39-class head, Softmax output, and top-k results.

Work activity

Reviewed the JSIEC dataset and the 39 disease/finding classes used by the prototype.

Learning / output

The documented development set contains 997 usable images across 39 class folders.

Work activity

Reviewed evaluation metrics and the label-mapping issue in the current prototype.

Learning / output

Noted the reported initial accuracy and that the permutation-adjusted result is diagnostic only; a defensible benchmark needs independently verified labels and a held-out test set.

Work activity

Reviewed the fundus-classification outputs and noticed the model was identifying some images incorrectly.

Learning / output

Recorded image misidentification as an issue to address before evaluating normal fundus images from the target device.

Work activity

Reviewed the model’s behavior against the goal of identifying normal fundus images from a specific device.

Learning / output

Kept the project’s 95% hit-rate target for normal-image identification as the performance goal.

Work activity

Worked with the model to identify the best checkpoint for fundus-image classification.

Learning / output

Checkpoint selection focused on improving image-identification performance ahead of the Oct 9 project review.

Work activity

Continued model and checkpoint work using the available compute setup.

Learning / output

Limited infrastructure meant each training cycle took about 3–4 hours; reviewed model outputs and checkpoint quality as part of the work.

Work activity

Met with Phani Krishna sir to set the next project goals.

Learning / output

Collect fundus images from a specific LVPEI source; test the model on LVPEI normal fundus images; tune model weights to improve the hit rate.

Planned activity

collect and organize fundus images from the specific LVPEI imaging device.

Expected output

Expected output: a device-specific image set ready for review and labeling.

Planned activity

review image quality, file formats, and available labels in the collected dataset.

Expected output

Expected output: a record of usable images, label availability, and any data gaps.

Planned activity

prepare the device-specific images using the model’s required input preprocessing.

Expected output

Expected output: consistent inputs for testing the fundus classification model.

Planned activity

check the disease-label mapping for the images selected for evaluation.

Expected output

Expected output: verified image-to-label records for interpreting model predictions.

Planned activity

test the current best checkpoint on LVPEI normal fundus images from the target device.

Expected output

Expected output: a baseline measure of normal-image identification performance.

Planned activity

inspect examples where the model’s image identification is incorrect or uncertain.

Expected output

Expected output: documented prediction errors to guide model adjustments.

Planned activity

group observed errors by image quality and disease/finding label, where labels are available.

Expected output

Expected output: a clearer view of recurring error patterns.

Planned activity

use the reviewed errors to decide which model weights or training settings to adjust.

Expected output

Expected output: a defined, trackable model-tuning experiment.

Planned activity

run a controlled model-tuning cycle using the available labeled project data.

Expected output

Expected output: a candidate checkpoint for comparison with the current best model.

Planned activity

compare candidate and current checkpoints on the same device-specific validation images.

Expected output

Expected output: checkpoint comparison based on consistent data and evaluation criteria.

Planned activity

measure normal-fundus identification against the project’s 95% hit-rate target.

Expected output

Expected output: observed hit rate and a record of whether the target was met.

Planned activity

evaluate disease/finding identification across the available labeled fundus images.

Expected output

Expected output: class-wise results that show where the model performs well or needs improvement.

Planned activity

review class-wise errors and the confusion matrix, if the evaluation labels support it.

Expected output

Expected output: prioritized failure cases for the next round of model refinement.

Planned activity

connect device-specific image input, preprocessing, model inference, and prediction display in one workflow.

Expected output

Expected output: an end-to-end prototype flow for fundus disease/finding classification.

Planned activity

summarize dataset coverage, evaluation results, selected checkpoint, and remaining gaps.

Expected output

Expected output: a project progress summary and next-step plan for improving device-specific disease identification.