Claims:1. A computer implemented system to detect a plurality of diseases from captured image modality , wherein the system comprises:
at least one processor; and one or more storage devices configured to store instructions configured for execution by the at least one processor in order to cause the system to:
receive a plurality `image of a patient;
identify a plurality of indicators throughout the plurality of image using a convolutional network;
the non-transitory computer readable storage medium configured to store a plurality of imageplurality of image application, wherein the plurality of image analysis application comprises:
a graphical user interface comprising a plurality of interactive elements configured to enable capture and analysis of the plurality of image via a user device;
a reception means, adapted to receive an input from an image capturing device based on a plurality of parameters of the image capturing device, wherein the input is the plurality of image of the patient;
an interactive plurality of image rendering means, adapted to dynamically render the input, wherein the dynamically rendered input is configurable accessible on the graphical user interface via the user device using the interactive elements; and
the plurality of image capture means, adapted to capture the plurality of image based on the dynamically rendered input.

2. The system as claimed in claim 1, comprises of an administrator module, wherein created for an admin to provide access to a plurality of client users like:
an institute module, created to give access to plurality of Institutes of their login by admin based on the requirement;
an uploader module, created to give access to plurality of uploaders to upload bulk images of their login by admin based on their requirement;
an annotator module, created to give access to plurality of annotators of their login to annotate the individual images by admin based on the requirement; and
a prediction module, created as a reporting tool that displays a disease based on the uploaded images using a convolutional network.

3. The system as claimed in claim 1, wherein the system detects at least one disease selected from such as diabetic retinopathy, glaucoma, pulmonary disease and breast cancer.

4. The system as claimed in claim 1 wherein the system detects at least one of a lesion, a venous beading, a venous loop, an intra retinal microvascular abnormality, an 25 intra retinal hemorrhage, a micro aneurysm, a soft exudate, a hard exudate, a vitreous/prerenal hemorrhage, neovascularization, pathological failure, TB (Tuberculosis), Pneumonia, etc or the like.

5. A computer implemented method to detect a plurality of diseases from captured images, wherein the said method comprises steps of:
providing, a plurality of image for analysis accessible by a user device via a graphical user interface of an image analysis module;
providing, a plurality of interactive elements on the graphical user interface of the plurality of image analysis module, the interactive elements configured to enable capture and analyze the plurality of images;
receiving, an input from an image capturing device based on a plurality of parameters of the image capturing device by the plurality of image analysis application via the graphical user interface, wherein the input is the plurality of image of the patient displayed in a live mode;
dynamically rendering, the input by the plurality of image analysis application via the graphical user interface, wherein the dynamically rendered input is configurable accessible on the graphical user interface via the user device using the interactive elements.
capturing, the plurality of image based on the dynamically rendered input by the plurality of image analysis application via the graphical user interface;
identifying, a plurality of indicators throughout the plurality of image using a convolutional network; and
detecting, a presence or absence of a retinal, TB, pneumonia, Lung Cancer and Breast Cancer based the identified indicators using the convolutional network; and
classifying, a severity of the retinal disease based on the presence or absence of the retinal disease using the convolutional network.

6. The method as claimed in claim 5, wherein the indicator is one of an abnormality, a retinal feature,, TB, pneumonia, Lung Cancer and Breast Cancer or the like.

7. The method as claimed in claim 5, wherein the abnormality is one of a lesion, a venous beading, a venous loop, an intra retinal microvascular abnormality, an intra retinal hemorrhage, a micro aneurysm, a soft exudate, a hard exudate, a vitreous/preretinal hemorrhage, neovascularization, pathological failure, TB (Tuberculosis), Pneumonia, etc or the like.
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