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Machine learning techniques that helps computers to learn from the skin disease images.

Machine learning techniques for the detection of Melasma Disease. - NIL

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2025/11/096946
Enrollment
277
Registered
2025-11-06
Start date
Unknown
Completion date
Unknown
Last updated
2025-11-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: L00-L99- Diseases of the skin and subcutaneous tissue

Interventions

Intervention1: Nil: Nil

Sponsors

Department of Dermatology Venereology and Leprosy
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: We will be doing the facial pigmentation of melasma region wherein we will concentrating on the melasma and the non-melasma regions in the face Adult patients are diagnosed with facial pigmentation and melasma

Exclusion criteria

Exclusion criteria: Patients unwilling for photography Those with facial dermatitis or other diseases overlying melasma

Design outcomes

Primary

MeasureTime frame
Accurate detection and classification of skin diseases using deep learning models.Timepoint: Baseline—

Secondary

MeasureTime frame
NilTimepoint: NIL—

Countries

India

Contacts

Public ContactDr. Smitha Prabhu S

Manipal Institute of Technology

srikanth.prabhu@manipal.edu06362147342

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026