Requirements

MOBAPP-001: The mobile application SHALL function on mobile devices running Apple iOS version 14.8 or higher.
MOBAPP-002: The mobile application SHALL function on mobile devices running Android iOS version 11 or higher.
MOBAPP-003: The mobile application SHALL require a username and password for authentication.
MOBAPP-004: The mobile application SHALL require two-factor authentication.
MOBAPP-005: The mobile application SHALL support biometric authentication.
MOBAPP-006: The mobile application SHALL determine if the attached camera is capable of capturing photos with the necessary resolution.
MOBAPP-007: The mobile application SHALL determine if the captured photo meets photo quality requirements.
MOBAPP-008: The mobile application SHALL securely transmit photos to the server environment.
MOBAPP-009: The mobile application SHALL encrypt captured photos at rest.
MOBAPP-010: The mobile application SHALL prevent captured photos from being accessed by other mobile applications on the device.
MOBAPP-011: The mobile application SHALL prompt the user as to the specific area of the body to be captured.
MOBAPP-012: The mobile application SHALL obscure the source of the photos.
MOBAPP-013: The mobile application SHALL enforce a strong password policy.

SERENV-001: The server environment SHALL accept connections from authenticated user accounts.
SERENV-002: The server environment SHALL persist photos for a maximum of 10 years.
SERENV-003: The server environment SHALL process uploaded photos in a FIFO manner.
SERENV-004: The server environment SHALL utilize machine learning algorithms to detect skin cancer.
SERENV-005: The server environment SHALL allow a secure connection to a customer’s HIS (Health Information System) for patient data tracking.

WEBPOR-001: The web portal SHALL allow a physician to review photos submitted by a patient.
WEBPOR-002: The web portal SHALL allow a physician to enter a diagnosis for an image that contains areas of concern.
WEBPOR-003: The web portal SHALL allow a physician to submit the diagnosis into their HIS (Health Information System).

FUN-001: The system SHALL utilize machine learning algorithms on all images provided by users to help with the analysis/identification of any suspected skin cancer diagnosis. The machine learning algorithms that shall be used for this analysis will be those of which are suitable for image classification and image recognition. Examples of these algorithms include support vector machines, decision trees, K Nearest Neighbor, and or algorithms related to neural networks.

FUN-002: The system SHALL utilize a client-server architecture. Due to the nature of this system and its chosen design, the system will benefit most from utilizing a client-server architecture where security, scalability, and handling numerous clients and computations in an efficient manner can be achieved.
FUN-003: The system SHALL be operable on mobile devices of various operating systems. The system shall be able to recognize touch gestures, perform the desired behaviors of the system, be able to utilize and access the mobile device’s tools and sensors in addition to storage. To clarify, the system shall be able to use the mobile device’s WIFI or data to connect to its server, acquire permissions to utilize the mobile device’s camera, and acquire permissions to read and write to the mobile device’s storage.
FUN-004: The system SHALL have a front-end graphical user interface that is easy to navigate, easy to use, organized, and should be following good design principles. It should be scalable and flexible between phones of different screen sizes and operating systems.
FUN-005: The system SHALL abide by security frameworks including the NIST cybersecurity framework, HIPAA, FDA medical device cybersecurity guidelines, OWASP Top 10 guidance, and CWE top 25 guidance.

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