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The major aim of this research is to identify the AML early from the morphological images. The proposed approach detects AML through four major stages, namely pre-processing, segmentation, feature extraction and classification. The pre-processing stage is employed for image enhancement. The segmentation process is utilized for segmenting the nucleus as well as the cell mask. When the human is affected by AML, the nucleus shape is asymmetrical, and texture is altered. The features achieved by the feature extraction process are given as the input of the classifier. The classification is attained by using the hybrid convolutional bidirectional LSTM-based recurrent neural network network. Although there are several optimisation algorithms proposed to solve different segmentation problems in the literature, to achieve the classified result more optimal, the slime mould optimisation algorithm is utilised. The data sets to detect AML were collected from Munich University Hospital. The experimental investigation is conducted, and from the experimental outcomes, it is examined that the proposed AML detection has obtained a sensitivity of 95.12%, a specificity of 93.72%, an accuracy of 97.95%, a precision of 94.64%, Matthews correlation coefficient of 98.2% and recall of 95.78%. This indicates that the proposed technique outperforms other state-of-art methods. [ABSTRACT FROM AUTHOR]