RESUMEN
In the medical field, some specialized applications are currently being used to treat various ailments. These activities are being carried out with extra care, especially for cancer patients. Physicians are seeking the help of technology to help diagnose cancer, its dosage, its current status, cancer classification, and appropriate treatment. The machine learning method developed by an artificial intelligence is proposed here in order to effectively assist the doctors in that regard. Its design methods obtain highly complex cancerous inputs and clearly describe its type and dosage. It is also recommending the effects of cancer and appropriate medical procedures to the doctors. This method ensures that a lot of doctors' time is saved. In a saturation point, the proposed model achieved 93.31% of image recognition, 6.69% of image rejection, 94.22% accuracy, 92.42% of precision, 93.94% of recall rate, 92.6% of F1-score, and 2178 ms of computational speed. This shows that the proposed model performs well while compared with the existing methods.
RESUMEN
In this article, we ponder multi-user medical image transmission using Cognitive Multiple-input Multiple-output (MIMO) Multi-carrier Code-division-multiple-access (MC-CDMA) system to monitor patient information. We investigate the performance of such system in the communication layer and application layer of internet of things (IOT). Patient monitoring system plays vital role in the hospital particularly in the emergency ward to resolve certain problems such as maintaining glucose level in the body, maintaining minimum sugar levels under emergency conditions. IOT find tremendous application in the hospital to deal with certain issues such as injection of drug to the patients by doctor from remote places, monitoring patients heart beats, sugar level by the concerned doctors. MIMO finds many applications in medical field to enrich data rate while communication patient information at faster rate. We utilize MC-CDMA system to accommodate large user patients information and to transmit such information with high resolution by eliminating channel impairments. We are utilizing Cognitive spectrum for medical image transmission of higher bandwidth applications. We realize Double-space-time transmit diversity as MIMO profile to boost up throughput. We perform multi-carrier modulation using IDFT at the transmitter .we carryout demodulation employing DFT at the hospital. We introduce Multi-carrier communication to fulfil the need of bandwidth efficiency and to diminish frequency selectivity effects.In the application layer, we estimate patient's information with aid of block-nulling decoding algorithm. Moreover we analyze the quality of image of D-STTD MC-CDMA system with turbo style of channel encoder to manifest medical image with high resolution with less signal strength. We conclude that Cognitive D-STTD MC-CDMA system provides reliable communication for the application of IOT and also transfer high resolution medical image with less signal strength in order to observe patient status by doctor.
