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Leukemia is a form of blood cancer that develops when the human body’s bone marrow contains too many white blood cells. This medical condition affects adults and is considered a prevalent form of cancer in children. Treatment for leukaemia is determined by the type and the extent to which cancer has developed across the body. It is crucial to diagnose leukaemia early in order to provide adequate care and to cure patients. Researchers have been working on advanced diagnostics systems based on Machine Learning (ML) approaches to diagnose leukaemia early. In this research, we employ deep learning (DL) based convolutional neural network (CNN) and hybridized two individual blocks of CNN named CNN-1 and CNN-2 to detect acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), and multiple myeloma (MM). The proposed model detects malignant leukaemia cells using microscopic blood smear images. We construct a dataset of about 4150 images from a public directory. The main challenges were background removal, ripping out un-essential blood components of blood supplies, reduce the noise and blurriness and minimal method for image segmentation. To accomplish the pre-processing and segmentation, we transform RGB color-space into the greyscale 8-bit mode, enhancing the contrast of images using the image intensity adjustment method and adaptive histogram equalisation (AHE) method. We increase the structure and sharpness of images by multiplication of binary image with the output of enhanced images. In the next step, complement is done to get the background in black colour and nucleus of blood in white colour. Thereafter, we applied area operation and closing operation to remove background noise. Finally, we multiply the final output to source image to regenerate the images dataset in RGB colour space, and we resize dataset images to [400, 400]. After applying all methods and techniques, we have managed to get noiseless, non-blurred, sharped and segmented images of the lesion. In next step, enhanced segmented images are given as input to CNNs. Two parallel CCN models are trained, which extract deep features. The extracted features are further combined using the Canonical Correlation Analysis (CCA) fusion method to get more prominent features. We used five classification algorithms, namely, SVM, Bagging ensemble, total boosts, RUSBoost, and fine KNN, to evaluate the performance of feature extraction algorithms. Among the classification algorithms, Bagging ensemble outperformed the other algorithms by achieving the highest accuracy of 97.04%.