الفهرس 
Chapter 1. IntroductionOnly 14 pages are availabe for public view
 |  | from | 142 |  |  |
| | | from | 142 | | |
Abstract
Human health can be adversely affected by tumors, inflammations,
fractures, and other bone diseases. Medical imaging using skeletal
scintigraphy images is one of the most significant and crucial methods to
detect bone diseases. Early diagnosis of bone diseases is very important in
the treatment phase of the patient. The diagnosis of skeletal scintigraphy
images depends on the expert, which requires a lot of experience, time, and
effort.
Dealing with Egyptian diseases is a major issue in this thesis. Bone
metastasis is common in Egypt, especially in Menoufia University Hospital.
The diagnosis of bone diseases varies for children, women, and men.
Furthermore, the main contribution is to develop a diagnosis algorithm for
bone diseases. So, three algorithms are proposed.
For enhancing the dark parts of the skeletal scintigraphy images, an
adaptive algorithm is proposed. It is built using the Salp Swarm Algorithm
(SSA) and a Neutrosophic Sets (NS) with several criteria. The optimum
improvement for each individual image is first determined using the SSA
algorithm, and the NS algorithm is used to determine the similarity score for
each image using the adaptive weight coefficients.
For segmenting the dark parts from the bone in the skeletal
scintigraphy images, a multi-threshold algorithm is proposed. It segments
the skull, the trunk, and the lower limbs separately rather than the whole
skeletal scintigraphy image. A sharpness index for each of the three parts is
evaluated, and the best threshold is computed using the SSA with a fitness
function based on maximizing the Tsallis entropy function for each part.
For diagnosing bone diseases in skeletal scintigraphy images, a multiclass
with mixed data types algorithm is proposed. It depends on segmented
organ images, patient data, and statistical features. Four phases are
involved, including pre-processing, feature extraction and selection using
the SSA with the age, gender, and organ name of the patient, modeling
phase, and classification phase. In the modeling phase, the segmented
organs image model relies on three self-attention layers with stacks of three
convolution layers, and the selected features of each organ model are
composed of Inception-V3 with stacks of three convolution layers and nine
inception modules. A single feature map is used to generate the different
output layers. The concatenation model is used to combine them and enable
the algorithm to learn new features with a dense layer. The classification
phase of fully connected layers with the SoftMax activation function is
applied to classify each organ into one of three classes: normal, tumor, or
inflammation.
These proposed algorithms are tested using different measures
according to each criterion. The efficiency of the proposed algorithms
achieved superior performance. The sharpness index of the enhancement
algorithm is 58.84058. The accuracy value of the segmentation algorithm is
96%. The accuracy value of the classification algorithm is 97.5% with a loss
value of 0.09.