CNN based Projects/Papers
- DNA Sequence Classification: a. DNA sequence classification for identifying genetic variations b. Gene function prediction from DNA sequences c. Classification of DNA sequences for disease risk assessment
- Lung Cancer: a. Lung nodule detection b. Lung nodule classification c. Lung cancer staging
- Breast Cancer: a. Mammogram analysis for breast cancer detection b. Breast cancer subtype classification c. Breast tumor segmentation
- Brain Tumor: a. Brain tumor detection b. Brain tumor segmentation c. Brain tumor subtype classification
- Diabetic Retinopathy: a. Retinal image analysis for diabetic retinopathy detection b. Diabetic macular edema detection c. Retinal vessel segmentation
- Skin Cancer: a. Melanoma detection and classification b. Non-melanoma skin cancer detection c. Skin lesion segmentation
- Cardiac Disease: a. Cardiac image analysis for heart disease diagnosis b. Left ventricle segmentation c. Coronary artery disease detection
- Bone Fracture: a. X-ray analysis for bone fracture detection b. Fracture localization and classification c. Orthopedic implant placement analysis
- Colon Polyp: a. Colonoscopy analysis for polyp detection b. Polyp segmentation and classification c. Adenoma detection in colonoscopy images
- Gastrointestinal Disease: a. Gastric lesion detection and classification b. Colorectal cancer detection c. Esophageal disease diagnosis
- Liver Disease: a. Hepatic lesion detection b. Liver fibrosis staging c. Liver tumor segmentation
- Prostate Cancer: a. Prostate tumor detection and segmentation b. Gleason score prediction c. Prostate cancer recurrence prediction
- Ophthalmic Diseases: a. Glaucoma detection and classification b. Age-related macular degeneration diagnosis c. Retinopathy of prematurity detection
- Pneumonia: a. Chest X-ray analysis for pneumonia detection b. Viral vs. bacterial pneumonia differentiation c. Pneumonia severity assessment
- Tuberculosis: a. Tuberculosis detection from chest imaging b. Tuberculosis lesion segmentation c. Tuberculosis drug resistance prediction
- COVID-19: a. Chest CT scan analysis for COVID-19 detection b. COVID-19 severity prediction c. COVID-19 image-based prognosis
- Alzheimer’s Disease: a. Brain MRI analysis for Alzheimer’s disease detection b. Hippocampus segmentation for Alzheimer’s disease progression assessment c. Alzheimer’s disease subtype classification
- Stroke: a. Brain MRI analysis for stroke detection b. Ischemic vs. hemorrhagic stroke differentiation c. Stroke lesion segmentation
- Arthritis: a. Rheumatoid arthritis detection and classification b. Osteoarthritis diagnosis c. Hand joint segmentation for arthritis assessment
- Scoliosis: a. Scoliosis detection and severity assessment b. Spine curvature measurement c. Scoliosis brace fitting analysis
- Kidney Disease: a. Kidney stone detection and classification b. Renal cyst segmentation c. Chronic kidney disease progression prediction
- Dental Caries: a. Dental X-ray analysis for caries detection b. Caries severity assessment c. Tooth segmentation and alignment analysis
- Cervical Cancer: a. Cervical cytology analysis for cancer detection b. Cervical lesion segmentation c. HPV-related cervical cancer risk prediction
- Esophageal Cancer: a. Esophageal endoscopy analysis for cancer detection b. Barrett’s esophagus diagnosis c. Esophageal tumor staging
- Ovarian Cancer: a. Ovarian tumor detection and classification b. Ovarian cyst analysis c. Ovarian cancer prognosis prediction
- Pancreatic Cancer: a. Pancreatic tumor detection and segmentation b. Pancreatic cyst analysis c. Pancreatic cancer survival prediction
- Thyroid Disease: a. Thyroid nodule detection and classification b. Thyroid ultrasound analysis c. Thyroid cancer risk stratification
- Melanoma: a. Dermoscopy analysis for melanoma detection b. Melanoma segmentation and classification c. Skin cancer metastasis prediction
- Ultrasound Imaging: a. Fetal ultrasound analysis b. Cardiac ultrasound analysis c. Abdominal organ segmentation
- Mammogram Analysis: a. Breast density assessment b. Microcalcification detection c. Breast cancer recurrence prediction
- Endoscopy Analysis: a. Polyp detection and classification in gastrointestinal endoscopy b. Gastric ulcer detection c. Early gastric cancer detection
- Echocardiogram Analysis: a. Left ventricular function assessment b. Cardiac abnormality detection c. Congenital heart disease diagnosis
- Histopathology Analysis: a. Lymphoma classification b. Renal biopsy analysis c. Breast histopathology image analysis
- Nuclear Medicine Image Analysis: a. Thyroid scintigraphy analysis b. Bone scan analysis for metastases detection c. Myocardial perfusion analysis
- Radiology Report Generation: a. Automated report generation from medical images b. Abnormality description and localization c. Report summarization and structured information extraction
- Bone Age Assessment: a. Bone age estimation from X-rays b. Growth prediction and evaluation c. Puberty stage assessment
- Embryo Quality Evaluation in IVF: a. Embryo selection for in vitro fertilization b. Morphological assessment of embryos c. Blastocyst grading and selection
- Fetal Ultrasound Analysis: a. Fetal biometry measurement b. Fetal anomaly detection c. Placental localization and analysis
- Respiratory Disease Detection: a. Chronic obstructive pulmonary disease (COPD) diagnosis b. Asthma detection and severity assessment c. Interstitial lung disease classification
- Retinal Vessel Segmentation: a. Automated segmentation of retinal blood vessels b. Retinal vessel tortuosity analysis c. Vessel caliber measurement
- Image-Guided Surgery and Interventions: a. Surgical instrument tracking b. Real-time tumor margin delineation during surgery c. Catheter and guidewire positioning guidance
- Bladder Cancer: a. Bladder tumor detection and segmentation b. Bladder cancer grading c. Recurrence prediction in bladder cancer
- Leukemia: a. Blood cell classification for leukemia diagnosis b. Blast cell detection and counting c. Leukemia subtype classification
- Liver Fibrosis: a. Liver fibrosis staging from histopathology images b. Fibrosis regression monitoring c. Non-invasive fibrosis assessment using imaging
- Osteoporosis: a. Bone mineral density analysis for osteoporosis diagnosis b. Fracture risk prediction c. Vertebral compression fracture detection
- Gastric Cancer: a. Gastric tumor detection and segmentation b. Gastric cancer staging c. Survival prediction in gastric cancer
- Inflammatory Bowel Disease (IBD): a. Crohn’s disease detection and classification b. Ulcerative colitis assessment c. Inflammation localization in IBD
- Multiple Sclerosis: a. Multiple sclerosis lesion detection and segmentation b. Disease activity monitoring c. Progression prediction in multiple sclerosis
- Retinopathy of Prematurity (ROP): a. Retinal vessel analysis for ROP detection and grading b. Plus disease assessment c. ROP treatment decision support
- Pancreatitis: a. Acute pancreatitis detection b. Pancreatic pseudocyst identification c. Pancreatic necrosis segmentation
- Celiac Disease: a. Celiac disease diagnosis from small bowel biopsy images b. Villi atrophy assessment c. Gluten-free diet compliance monitoring
- Atrial Fibrillation: a. ECG analysis for atrial fibrillation detection b. Arrhythmia classification and diagnosis c. Stroke risk prediction in atrial fibrillation patients
- Glomerulonephritis: a. Glomerulus segmentation and classification b. Kidney biopsy analysis for glomerulonephritis diagnosis c. Disease activity assessment in glomerulonephritis
- Myocardial Infarction: a. Electrocardiogram (ECG) analysis for myocardial infarction detection b. Ischemic zone segmentation c. Infarct size quantification
- Retinal Detachment: a. Retinal image analysis for retinal detachment detection b. Identification of retinal breaks c. Assessment of retinal reattachment success
- Glioma: a. Glioma tumor detection and segmentation b. Glioma grading and subtyping c. Prediction of glioma patient survival
- Hematoma: a. Hematoma detection and segmentation from brain scans b. Hematoma volume quantification c. Localization of intracranial bleeding
- Gastric Ulcer: a. Endoscopy image analysis for gastric ulcer detection b. Ulcer size estimation c. Ulcer healing monitoring
- Atrial Fibrillation: a. ECG analysis for atrial fibrillation detection b. Arrhythmia classification c. Atrial fibrillation risk prediction
- Hematoma Detection: a. Detection of intracranial hematoma from head CT scans b. Hematoma segmentation and quantification c. Hemorrhage localization in trauma cases
- Myocardial Infarction: a. ECG analysis for myocardial infarction detection b. ST-segment elevation identification c. Infarct localization from cardiac imaging
- Cystic Fibrosis: a. Lung disease assessment in cystic fibrosis b. Bronchiectasis detection and monitoring c. Mucus plugging identification
- Deep Vein Thrombosis (DVT): a. DVT detection and localization from ultrasound images b. Thrombus segmentation and characterization c. DVT risk prediction in high-risk patients
- Hepatitis: a. Hepatitis detection and classification b. Hepatic fibrosis staging c. Viral hepatitis genotype identification
- Endometriosis: a. Endometriosis lesion detection and segmentation b. Endometrioma characterization c. Deep infiltrating endometriosis identification
- Retinal Detachment: a. Retinal detachment detection from fundus images b. Macula involvement assessment c. Surgical planning for retinal detachment repair
- Glomerulonephritis: a. Glomerulonephritis diagnosis from renal biopsy images b. Glomerular segmentation and quantification c. Disease activity monitoring
- Pulmonary Embolism: a. Pulmonary embolism detection from chest CT scans b. Embolus segmentation and localization c. Risk stratification in pulmonary embolism cases
- Glioma: a. Glioma tumor segmentation from brain MRI scans b. Glioma subtype classification c. Prediction of treatment response in glioma patients
- Amyotrophic Lateral Sclerosis (ALS): a. ALS disease progression monitoring from muscle imaging b. Muscle atrophy detection and quantification c. Survival prediction in ALS patients
- Aortic Aneurysm: a. Aortic aneurysm detection from medical imaging b. Aneurysm size and growth rate measurement c. Risk assessment for aortic rupture or dissection
- Influenza: a. Detection and classification of influenza strains b. Severity prediction in influenza cases c. Analysis of transmission patterns and outbreaks
- Psoriasis: a. Psoriasis lesion segmentation and quantification b. Psoriatic arthritis detection and classification c. Assessment of treatment response in psoriasis patients
- Retinitis Pigmentosa: a. Retinal structure analysis for retinitis pigmentosa diagnosis b. Disease progression monitoring c. Genetic mutation classification in retinitis pigmentosa
- Uterine Fibroids: a. Uterine fibroid detection and segmentation from ultrasound images b. Quantification of fibroid characteristics c. Prediction of fibroid-related symptoms and treatment response
- Polycystic Kidney Disease (PKD): a. Renal cyst detection and segmentation in PKD b. Kidney volume measurement for disease progression c. Risk prediction of renal complications in PKD patients
Eye Related
- Cataracts: AI can assist in diagnosing and grading cataracts by analyzing images of the eye lens.
- Glaucoma: AI algorithms can aid in early detection and monitoring of glaucoma by analyzing optic nerve and visual field data.
- Diabetic retinopathy: AI can detect signs of diabetic retinopathy by analyzing retinal images, enabling early intervention.
- Macular degeneration: AI algorithms can help identify and track the progression of age-related macular degeneration (AMD) through retinal imaging.
- Retinoblastoma: AI can assist in the early detection of retinoblastoma, a rare form of eye cancer, by analyzing retinal images.
- Dry eye syndrome: AI can help diagnose and monitor dry eye syndrome by analyzing tear film dynamics and ocular surface characteristics.
- Amblyopia (lazy eye): AI-based vision screening tools can aid in detecting amblyopia in children by analyzing visual acuity and stereoscopic vision.
- Strabismus: AI algorithms can help assess eye alignment and detect strabismus, a condition where the eyes are misaligned.
- Refractive errors: AI can assist in the precise measurement of refractive errors like myopia, hyperopia, and astigmatism, improving accuracy in prescribing corrective lenses.
- Retinal detachment: AI can aid in the early detection of retinal detachment by analyzing retinal imaging and identifying subtle changes.
- Optic neuritis: AI algorithms can assist in the diagnosis of optic neuritis, an inflammation of the optic nerve, by analyzing visual field data and optic disc appearance.
- Color blindness: AI-based color vision tests can help assess color vision deficiencies and distinguish between different types of color blindness.
- Retinitis pigmentosa: AI can assist in diagnosing and monitoring retinitis pigmentosa, a progressive degenerative eye disease, through retinal imaging analysis.
- Keratoconus: AI algorithms can aid in diagnosing and monitoring keratoconus, a corneal disorder, by analyzing corneal topography and biomechanical properties.
- Uveitis: AI can help detect and monitor uveitis, an inflammation of the uvea, by analyzing ocular imaging and identifying characteristic patterns.
- Corneal dystrophy: AI algorithms can assist in identifying and categorizing different types of corneal dystrophy based on clinical images and patient data.
- Retinal vascular diseases: AI can help detect and classify retinal vascular diseases such as retinal vein occlusion, diabetic macular edema, and hypertensive retinopathy using retinal imaging analysis.
- Optic disc edema: AI algorithms can aid in detecting optic disc edema, which can be a sign of various conditions such as papilledema or optic neuritis.
- Ocular tumors: AI can assist in the detection and classification of ocular tumors by analyzing imaging data and identifying abnormal tissue patterns.
- Ocular trauma: AI algorithms can help assess the severity and identify specific injuries in cases of ocular trauma through image analysis.
- Corneal ulcers: AI can aid in diagnosing and monitoring corneal ulcers by analyzing corneal imaging and identifying characteristic features.
- Ptosis (drooping eyelid): AI can help assess and quantify the degree of ptosis, assisting in the evaluation and monitoring of this condition.
- Optic nerve hypoplasia: AI algorithms can aid in diagnosing optic nerve hypoplasia by analyzing imaging data and identifying characteristic features.
- Ocular surface disorders: AI can assist in diagnosing and managing various ocular surface disorders like keratitis and conjunctivitis by analyzing clinical images and symptoms
Board Area :
- Medical imaging analysis
- Radiology and diagnostic support
- Pathology and histopathology analysis
- Automated tumor detection and classification
- Computer-aided diagnosis (CAD)
- Predictive analytics for disease outcomes
- Drug discovery and development
- Genomics and personalized medicine
- Clinical decision support systems
- Electronic health record (EHR) management
- Natural language processing for medical documentation
- Medical data mining and knowledge discovery
- Disease risk assessment and prevention
- Clinical trial design and optimization
- Medical chatbots and virtual assistants
- Telemedicine and remote patient monitoring
- Health monitoring wearables and sensors
- Predictive modeling for patient deterioration
- Surgical planning and simulation
- Robot-assisted surgery
- Prosthetics and orthotics design
- Rehabilitation and physical therapy
- Health behavior monitoring and intervention
- Medical fraud detection and prevention
- Patient sentiment analysis
- Mental health assessment and support
- Sleep disorder analysis and management
- Emergency room triage and resource allocation
- Chronic disease management
- Predictive analytics for hospital readmissions
- Population health management
- Public health surveillance and outbreak prediction
- Precision medicine and targeted therapies
- Clinical trial participant recruitment and matching
- Health informatics and data interoperability
- Medication adherence monitoring and reminders
- Drug dosage optimization
- Radiomics and radiogenomics
- Medical billing and coding automation
- Healthcare resource allocation and optimization
- Clinical workflow optimization
- Hospital bed management and patient flow
- Medical supply chain management
- Health chatbots for health education and awareness
- Digital therapeutics and mobile health applications
- AI-guided genetic counseling
- Oncology treatment planning
- Medical education and training simulations
- Pharmacovigilance and adverse event monitoring
- Medical research data analysis and interpretation