As the brain diseases are tremendously increasing nowadays, early diagnosis of brain diseases is very important. Detecting neurological disorders such as brain tumors using magnetic resonance imaging (MRI) has become an important research topic. Recently many machine learning and deep learning models have been proposed to detect and classify the brain abnormalities. Many of these models have high time complexity and still efficient models are required. The proposed model makes use of a Novel and Low Complexity Approach to solve the problem of classification of brain images. This approach is a less complexity deep learning model which uses novel methods for Denoising, Segmentation, Feature extraction and Classification of brain tumors. Here, it makes use of the advantages of bit plane approach and a unique feature extraction method. The proposed model makes use of the data set from Kaggle in which, size of the training data set is 2870 with four classes namely No Tumor, Glioma Tumor, Meningioma Tumor and Pituitary Tumor. The size of the testing data set is 394. A feature vector which matches most with the feature vector of the input image is considered as the class of the input image. The proposed method makes use of advantages of time domain and able to give good results. The overall performance of the proposed algorithm considering both training and testing data set is 97.34%. The proposed idea is comparable with the many existing models and the results are compared with three models CNN, VGG19 and Inception-V3 models and found to be promising.