Tumor forecast is as yet trying for the redesigned and current clinical innovation. Indeed, even now the explanation and all out restoring treatment or method of tumor isn't developed, after exploration performed on part of individuals influenced by cerebrum tumor some broad indications and its belongings are recognized. In view of finding it is anything but crucial to anticipate treatment. The entirety of the sorts of cerebrum tumor is formally renamed by the WHO. More than 120 kinds of mind tumors are identified, practically every type is having same manifestations and it’s hard to foresee the analysis. We have proposed tumor expectation framework dependent on Deep Learning innovation utilizing the hybrid method of Convolution Neural Networks (CNNs) with EfficientNET CNNs. The calculation of this method is to take care of tumor forecast issues with the current image dataset. Thus the datasets with the manifestations and impacts are dissected and to give the previous admonition to the patients and it is likewise a lifeline to the patients.

Prolonged use of mobile devices and poor posture have significantly increased the risk of neck discomfort among users. To address this concern, ergonomic optimization combined with advanced machine learning can help identify and mitigate musculoskeletal risks. However, accurately predicting individual ergonomic risk factors remains challenging due to varied device interactions, usage behaviors, and anatomical differences. This study proposes a novel machine learning framework for predicting and preventing neck pain in mobile device users by quantitatively forecasting a neck strain risk score based on sensor-derived ergonomic features. The approach utilizes the NeckCheck dataset, applies notch filtering for data pre-processing, and extracts features using the Histogram of Oriented Gradients for the neck region. Prediction is performed using an Enhanced Support Vector Regression (ESVR) model, with parameters tuned by the Shuffled Shepherd Optimization Algorithm (SSOA) to minimize error and maximize accuracy. Simulation results demonstrate that the proposed framework achieves superior prediction accuracy and lower root mean square error (RMSE) compared to conventional models, supporting its effectiveness in reducing ergonomic risk and preventing neck pain. This work presents a methodological framework for estimating neck-related ergonomic risk using motion-based features and optimization-enhanced regression modelling. The study focuses exclusively on algorithmic development and computational validation using the NeckCheck dataset, without involving direct clinical trials or physician-verified assessments. Accordingly, the findings should be interpreted as model-level evidence rather than clinical diagnostic validation.