BMS College of Engineering

BMS College of Engineering

TB Detection System

Major Project · Phase 2 · Department of CSE

BMSCE logo BMSCE /MAJOR PROJECT PHASE 2PHASE 2
Research prototype · v1
BMSCE logo BMSCE / HEALTH AI
Screening workflow
01 / 09
BMSCE logo BMSCE / HEALTH AI
About the project

02 / About

About
the project.

An explainable deep-learning prototype that screens frontal chest X-rays for TB-consistent abnormalities.

800Real chest X-rays
DenseNet-121Transfer-learned backbone
0.892Holdout ROC-AUC
2Independent hospital sources

The system

What it does.

  • Binary screeningNormal vs TB-consistent abnormality
  • Image-onlyThe X-ray decides — form answers never shift the score
  • ExplainableGrad-CAM overlay shown beside every result
  • Calibrated outputProbability plus a documented operating threshold
  • Quality-awareWarns when an image is unfit for reliable screening
  • Privacy-firstProcessed in memory; nothing stored by default

How it works

The pipeline.

  1. 01IngestPNG / JPG / DICOM, validated on upload
  2. 02NormaliseGrayscale, aspect-preserving pad, 224 × 224
  3. 03EnhanceCLAHE & lung-ROI tested as ablations, not assumed
  4. 04ClassifyDenseNet-121, ImageNet weights, sigmoid head
  5. 05ThresholdScreening flag from a pre-declared cut-off
  6. 06ExplainGrad-CAM heat map over the original X-ray

The ML model

Under the hood.

  • ArchitectureDenseNet-121 · ImageNet transfer learning
  • HeadSingle sigmoid output, BCE with positive weighting
  • Input224 × 224, grayscale → 3-channel, ImageNet-normalised
  • OptimiserAdamW · lr 2e-5 · weight decay 1e-4 · seed 42
  • DataShenzhen 662 + Montgomery 138 · 652 train / 148 held out
  • AugmentationMild affine & brightness — training split only
  • BaselinesResNet-50 and EfficientNet-B0 for comparison

Held-out results · threshold 0.50

SetSens.Spec.AUC
Shenzhen n=12070.5%94.9%0.892
Montgomery n=2841.7%100%0.828

Montgomery is a fully external source. The sensitivity drop is reported, not smoothed over — it is exactly why this stays a screening prototype.

Honest limits

What it cannot claim.

  • Not a diagnosis — screening support only
  • Two hospital sources; multi-site validation pending
  • Operating threshold not yet fixed on a validation split
  • Grad-CAM explains attention, not clinical correctness

The team

Meet the team.

Dept. of Computer Science & Engineering · Guide: Prof. Vikranth B.M

Nihaan Jonas

01 / Developer

Nihaan Jonas

1BM23CS213 · Sec 7D

Lakshith P

02 / Data & ML

Lakshith P

1BM23CS164

Kishen Deepak

03 / Report & design

Kishen Deepak

1BM23CS152 · Sec 7C

K C Sai Nithin

04 / Testing & research

K C Sai Nithin

1BM23CS130 · Sec 7C

Research demonstration for academic assessment. Not a medical device and not for clinical or treatment decisions.