Time series based treatment effect evaluation method and electronic device

Abstract

The invention discloses a time series based treatment effect evaluation method. The time series based treatment effect evaluation method comprises the steps of: acquiring lung CT image data, wherein the lung CT image data is composed of a plurality of lung sections; performing pre-processing on lung CT image data; designing a lung nodule detection model according to the pre-processed lung CT imagedata; designing a lung nodule classification model; designing a time axis based lung cancer prediction model according to the lung nodule detection model; and training the lung nodule detection modelaccording to the number of samples per batch. Based on the deep learning, a time series is added, the method dynamically analyzes the changes of the lung nodules, so that a doctor can more accuratelydiagnose a treatment effect of the lung cancer and predict the early recurrence. The diagnosis time of the algorithm is far less than the doctor's manual diagnosis time, and the important steps of the doctor's diagnosis can be assisted in a diagnosis process, which can save a lot of time and energy of the doctor.

Description

technical field [0001] The invention relates to imaging technology, in particular to a treatment effect evaluation method and electronic equipment based on time series. Background technique [0002] Lung cancer is one of the malignant tumors with the highest incidence rate among malignant tumors, and its five-year survival rate is only about 15%. During the treatment and reexamination of patients, doctors need to review CT images of multiple regular physical examinations to understand the change process of pulmonary nodules, the treatment effect of lung cancer, and the probability of lung cancer recurrence. When doctors study and judge each nodule of a patient, they have to track and evaluate the change of each nodule in time, which consumes a lot of time and energy, and it is easy to miss diagnosis and misdiagnose. [0003] Someone proposed the use of computer-aided diagnosis in the 1960s, using the large amount of computing power of the computer to help doctors diagnose. ...

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Problems solved by technology

[0004] In order to overcome the deficiencies in the prior art, one of the purposes of the present invention is to provide a time-series-based therapeutic effect evaluation method, which can solve the problem of low artificial diagnosis rate in the prior art

[0005] The second object of the present invention is to improve an electronic device, which can solve the problem of low manual diagnosis rate in the prior art

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