Magnetic Resonance Imaging (MRI) inputs are most noticeable in diagnosing brain tumors via computer and manual clinical understanding. Multi-level detection and classification of the images utilizing computer-aided processing depend on labels and annotations. Though the two processes are dynamic and time-consuming, without which the precise accuracy is less assured. For augmenting the accuracy in processing un-labeled or annotation-less images, this article introduces Absolute Classification-Detection Model (AC-DM). This model uses a conventional neural network for training the morphological variations proficient of achieving label-less classification and tumor detection. The traditional neural network trains the images based on differential lattice morphology for classification and detection. In this process, training for the lattices and their corresponding gradients is validated to improve the precision of the regional analysis. This helps to retain the precision of identifying tumors. The variations are recognized for their lattice mapping in the detected boundaries of the input image. The detected boundaries help to map accurate lattices for adapting morphological transforms. Thus, the partial and complex processing in detecting tumors is restrained in the suggested model, adapting to the classification. The efficiency of the suggested model is verified utilizing accuracy, precision, sensitivity, and classification time.