Blood Vessel Detection on Retinal Image Using Image Processing | Matlab Project With Source Code | Final Year Project Code

ABSTRACT

        Detection of blood vessel plays an important stage in different medical areas, such as ophthalmology, oncology, neurosurgery and laryngology. The significance of the vessel analysis was helped by the continuous overview in clinical studies of new medical technologies intended for improving the visualization of vessels. Blood vessel detection are commonly used in many field especially medical field. But recognition of blood vessels is a main troublesome in the automatic processing of retinal image In order to make work smoother and help patient in detecting their problems. The detection of blood vessel using retinal really helping to detect some disease such as diabetic and glaucoma. The recognition of blood vessels is a main troublesome in the automatic processing of retinal image. The diagnosis of cardiovascular and ophthalmological illnesses such as diabetic retinopathy and glaucoma is considered to be critical. Diabetic retinopathy is a complication of diabetes which affects the eyes. Manual analyses are conducted periodically by reviewing a patient's photographs, as not all images show signs of diabetic retinopathy. Accurate retinal blood vessel identification is therefore essential. 

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Detection of Skin Disease Symptoms and Treatment Suggestion Matlab Project With Source Code | Final Year Project Code

 ABSTRACT

         Skin cancer also known as melanoma it is one of the deadliest form of cancer if not recognized in time. Since the pigmented areas/moles of the skin can be nicely observed by simple, non-invasive visual inspection the clinical protocols of its recognition also consider several visual features. Melanoma is the deadliest form of skin cancer, which is considered one of the most common human malignancies in the world. Early detection of this disease can affect the result of the illness and improve the chance of surviving. The tremendous improvement of deep learning algorithms in image recognition tasks promises a great success for medical image analysis, in particular, melanoma classification for skin cancer diagnosis. Activation functions play an important role in the performance of deep neural networks for image recognition problems as well as medical image classification. Melanin is the pigment that discerns the color of human skin. The special cells produce melanin in the skin. If these cells are damaged or unhealthy, skin discoloration is visible. Skin pigment discoloration on cheeks is a hazardous fact as a symptom of human skin disease with a possibility of losing natural beauty. The extracted information of the skin discoloration can work as a guide to diagnosis the disease. In this research, different imaging techniques like preprocessing method, segmentation and morphological operations are used to analyze and extract the information of cheek’s discoloration lesion by measuring the pixel number of lesion on skin.

PROJECT OUTPUT

PROJECT DEMO VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

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Maize Leaf Disease Detection Using Image Processing Matlab Project With Source Code | Final Year Project Code

 ABSTRACT

         Crop disease protection is important for global food security, while the recognition of crop diseases at early stage is the key part of disease protection. The traditional identification and detection of crop leaf diseases is carried out by agricultural technicians. The identification and diagnosis of crop leaf disease is of great significance to improve the quality of crop cultivation. Compared with the traditional manual diagnosis method, the automatic identification of crop leaf disease based on computer vision technology has high efficiency and no subjective judgment error. But the traditional image processing technology is affected by different illumination conditions, cross shading. The algorithm's robustness is affected. Because deep learning dose not need to set learning features manually, which greatly improves the recognition efficiency. This project is developed in matlab using image processing deep learning CNN algorithm.

PROJECT OUTPUT

PROJECT VIDEO

Contact:

Prof. Roshan P. Helonde

Mobile: +91-7276355704

WhatsApp: +917276355704

Email: roshanphelonde@rediffmail.com

Read More