  Lung cancer is a severe menace to human health, due to which millions of
people die because of late diagnoses of cancer; thus, it is vital to detect the
disease as early as possible. The Computerized chest analysis Tomography of
scan is assumed to be one of the efficient solutions for detecting and
classifying lung nodules. The necessity of high accuracy of analyzing C.T. scan
images of the lung is considered as one of the crucial challenges in detecting
and classifying lung cancer. A new long-short-term-memory (LSTM) based deep
fusion structure, is introduced, where, the texture features computed from lung
nodules through new volumetric grey-level-co-occurrence-matrices (GLCM)
computations are applied to classify the nodules into: benign, malignant and
ambiguous. An improved Otsu segmentation method combined with the water strider
optimization algorithm (WSA) is proposed to detect the lung nodules. Otsu-WSA
thresholding can overcome the restrictions present in previous thresholding
methods. Extended experiments are run to assess this fusion structure by
considering 2D-GLCM computations based 2D-slices fusion, and an approximation
of this 3D-GLCM with volumetric 2.5D-GLCM computations-based LSTM fusion
structure. The proposed methods are trained and assessed through the LIDC-IDRI
dataset, where 94.4%, 91.6%, and 95.8% Accuracy, sensitivity, and specificity
are obtained, respectively for 2D-GLCM fusion and 97.33%, 96%, and 98%,
accuracy, sensitivity, and specificity, respectively, for 2.5D-GLCM fusion. The
yield of the same are 98.7%, 98%, and 99%, for the 3D-GLCM fusion. The obtained
results and analysis indicate that the WSA-Otsu method requires less execution
time and yields a more accurate thresholding process. It is found that 3D-GLCM
based LSTM outperforms its counterparts.
