INTRODUCTION
Voguet et al. (2009) noticed that patient's
age and positive margins can be considered as predictive factors for residual
tumor. Two expert centers conducted breast cancer test experiments involving
294 patients. A comparative analysis was carried between residual and non-residual
tumors. It was identified that 202 patients has residual tumor keeping the clinical
risk factors included. They concluded that positive margins on operative specimen
with addition of younger age are some risk factors for this type of tumor. Internecine
neoplasm proliferates over abutting areas and inducements the increase in annihilation
in women. The woman breast is composes of major three components including milk
producing glands, fatty tissues and blood vessels. There is another collection
of immune cells that are small bean shaped and carry fluids. Internecine masses
can harm these sensitive guard agents and further proliferating to other regions.
Application of Computer Aided Diagnostic (CAD) tools and methods brings healthy
source of inspiration for espial of internecine carcinomas (Voguet
et al., 2009). On an average, the CAD systems convert the laser signals
into digital ones for processing by microprocessor. Some better comparisons
can be made between mammograms obtained from conventional ways and CAD's.
More discursive focus can be made on specific regions (abuts of carcinoma cells).
An interesting study of 84 cases phyllodes (Foxcroft et
al., 2007) revealed 71 as benign, 5 as malignant and 8 borderlines.
Ultrasound and mammography were termed being non specific. The study showed
that accuracy is better analyzed in small carcinomas because larger ones need
more sampling. It was further noticed that fibro-adenomas had to be reviewed
using ultrasound. The antediluvian espial can better help the chances of recovery
and focuses the need to develop some improved methodologies for espial and diagnosis
(Akhtar et al., 2008). Several different techniques
are being employed for espial of internecine masses. Among these, mammography
is considered as a suitable approach for carcinoma espial. Also, there is a
need to improve the contrivances both in terms of X-ray and mammography itself
by enhancement of mammogram images.
Ultrasound is considered another suitable imaging approach for espial of those masses that can remain hidden in normal mammography techniques. Sonogram is a resulting picture produced recording the echoes (by application of high quality sound waves). There are some tinny calcium collections that may remain hidden from ultrasound, so this methodology may not be recommended in antediluvian or routine checking. Another enhanced approach is digital mammography in which the images are manipulated on computer systems instead of films. There is a trade-off between digital and normal mammography. Digitization can help in better understanding but does not guarantee the choicest solution.
A population based study of 356 patients concluded that tumor location is not
an independent prognostic aspect leading to survival. Besides, some critical
factors such as patient age, tumor size, central location, number of positive
lymph nodes and histological type associate a major risk of death. It was revealed
by experiments that location does not influence survival (Jayasinghe
and Boyages, 2009). Hassouna et al. (2006)
reported another interesting case study of 106 patients with age margin of 39.5
years and mean tumor size of 83 mm. The study revealed the comparative difference
of prognosis and treatment for phyllodes. Overall, 82 patients were treated
using conservative and 24 by radical surgery. During the observation, 8 patients
developed metastases and it was recommended that malignant phyllodes tumors
for a simple mastectomy and wide excision for benign and borderline. Mohamad
et al. (2009a) proposed a two stage gene selection method. This selection
method selects a smaller subset of useful gene. The automatic yield of this
smaller gene is a result of application of filters that perform some kind of
pre-selection. Further optimization was made through multi-objective integrated
method. They tested the phenomenon with three different datasets of gene expression
data. The approach was applicable to micro-array gene expression datasets that
can estimate the degree of comparison between cancerous and normal sets. Mohamad
et al. (2009b) highlighted the problems in micro-array data analysis
and comparison between cancerous and normal cells. The proposed approach overcame
all the hurdles (removal of noisy data, irrelevant gene data etc.) in order
to select a nice smaller subset of dataset for cancer classification. Basically,
the approach is a cyclic hybrid technique. Authors have used five real time
datasets to test the mechanism. Mohamad et al. (2010)
proposed a three stage method for selection of information genes to classify
cancer. The problem highlighted is the selection of set of disease genes from
some huge amount of genetic data over micro-array. The proposed method comprises
of initial selection by a filter technique, implementation of an integrated
method for optimization and frequency analysis of gene appearance in diverse
near optimal genetic subsets. This contribution helps classifier to accurately
classify the patterns. A critical review of literature indicated that the information
on the classification and prediction of neoplasm using neural network is inadequate.
Therefore, the main objective of present investigation was to propose an enhanced
approach for classification and prediction of neoplasm using neural network.
THE PROPOSED ENHANCED APPROACH
Neoplasm classification and prediction were proposed with the help of neural
network with prior data segmentation and purification and the selection of best
parameters for a feasible network. The proposed approach in this study was composed
of the following tasks.
| • |
Purification and segmentation |
| • |
Development of feasible network architecture |
| • |
Neoplasm classification |
| • |
Neoplasm prediction |
| • |
Flexibility |
The context of development of a network from a pool of choices in allowable feasible range is very important factor for approaching optimum results. This extreme optimal solution is worthless without adding flavors of best clustering, cleansing and scalability.
PURIFICATION AND SEGMENTATION
This stage of analysis contains the following points.
| • |
Data gathering |
| • |
Purification/cleanliness |
| • |
Textual to numeric conversion |
| • |
Data integration |
| • |
Segmentation/clustering |
The neoplasm datasets contain clinical data in terms of parameters Diagnosis, Radius, Texture, Perimeter, Area, Smoothness, Compactness, Concavity, Concave Points, Symmetry, Fractal Dimension, Single Epithelial Radius, Single Epithelial Texture, Single Epithelial Perimeter, Single Epithelial Area, Single Epithelial Smoothness, Single Epithelial Compactness, Worst Radius, Worst Texture, Worst Perimeter, Worst Area, Worst Smoothness, Worst Compactness, Worst Concavity, Worst Concave Points, Worst Symmetry and Worst Fractal Dimension described in Table 1.
On the other hand, Table 2 shows the snap of final shape
of data after vital adjustments. This translation helped to achieve the objectives
to better train the network system and better efficiency in terms of optimal
solution. It also helped in reduction of overhead involved in automatic dataset
translation by central process. As a result of this translation, complex architectures
were avoided by selecting simpler and smart combinations with less number of
layers and neurons.
The segmentation phase clustered the dataset in random clusters for training and testing the selected choices of combinations. Figure 1 describes the segmentation of datasets for neoplasm training and testing at random sub-sets. Dataset were fragmented into four chunks of unequal lengths. Each neoplasm segment contained four clusters and each cluster contained four sub-clusters. One larger cluster was chosen with a smaller cluster for neoplasm training architecture, while another larger and smaller for testing. This combination of sub-systems guaranteed the optimal solution of the study approach.
DEVELOPMENT OF FEASIBLE NETWORK ARCHITECTURE
Several combinations were generated to build, test and apply cluster suitability
for this approach.
|
| Fig. 1: |
Data clusters of neoplasm for training and testing |
| Table 1: |
Neoplasm dataset |
 |
|
| Table 2: |
Purified dataset |
 |
Since the neural network requires training over specimen datasets, variant
combinations were fed by thresh holding hidden layers with different neurons.
Only those architectures were listed that provided the most significant value
for root Mean Square Error (MSE). The selected architectures are described in
Table 3.
Table 3 describes 8 cases representing random number of sigmoid
layers with different neurons at each layer. It can be observed that with increasing
hidden layers and number of neurons on these specified layers, the root mean
square value also increased and decreased with the decrease in number of hidden
layers and an increase in neurons.
| Table 3: |
Random combination of layers with neurons against MSE |
 |
|
Based on this, it was concluded that case 7 is the chosen architecture over
specified clusters of datasets.
NEOPLASM CLASSIFICATION
With the selection of smart and efficient network, the training and cross validation of clusters were performed. For instance, Fig. 2 demonstrates that confusion matrix provided 96% of Internecine and 99.45% of benign diagnosis. The diagonal values from left to right (upper to lower) present numerals for benign and malignant confusion, respectively while numeral from right to left (upper to lower) depict corresponding errors values in training combinations of larger with smaller clusters.
Figure 3 describes the cross validation confusion matrix values. It is evident that Malignancy in cluster datasets was 100% and the benign ratio was 97.64%. The diagonal errors prune to be 2% for benign clusters and 0% for malignant chunks. However, a slight variation was noticed in numerical values for the chosen architecture against classification phenomenon. This might be due to minute difference in size of clusters at network training and classification phases.
Figure 4 shows the depiction of rates of deviation of training
and cross validation error values. There was a slight variation in Training
(T) and Cross Validation (CV) at the start of classification and both curves
behaved in same fashion after some 200 epochs. However, smaller differences
in curves present best classification in clusters and demonstrated an optimal
combination of sigmoid and neurons for T and CV.
|
| Fig. 2: |
Training confusion matrix |
|
| Fig. 3: |
Cross validation confusion matrix |
|
| Fig. 4: |
Mean square error in T and CV |
On other hand, root mean square error value range is below 0.1 (an average
value below 0.5 is considered suitable) which demonstrated very good classification
over trained and tested clusters.
NEOPLASM PREDICTION
Contrary to classification, predictions were made over 20% larger and smaller data chunks and generalized the idea for rest of clusters as there was a slight variation (normally less than 0.5%) in cross validation over entire domain and selected regions. Mean Square Error (MSE) values for training and cross validation over prediction are presented in Fig. 5. An average cost of criterion for MSE is below 0.03 and 0.05 for CV. These results were very optimal for prediction phenomenon. This idea was generalized to cover not only similar clusters in same domain but also to cover diversity of clusters in other domains (larger and smaller).
Figure 6 shows training and cross validation curves which were similar in behavior after about 170 epochs and slightly different in range between 60 to 150 epochs. Both curves remained side by side starting from 0.5 and declined continuously ending similarly along the x-axis after 200 epochs. The MSE values were highly optimal and desired for prediction of neoplasm in clinical clustered datasets.
FLEXIBILITY
This enhanced approach for neoplasm classification and prediction is highly flexible and can be scaled to any problem size. Figure 7 presents the enhanced approach with flexibility feature to accommodate any problem size. The first rectangle “DS” represents neoplasm actual dataset with its length and complexity. Last small rectangles show segments containing larger and smaller clusters. Processing elements define neoplasm classification and prediction in terms of parameters established for this approach (cost factors, confusion matrices, training, testing and percentage of malignant and benign identification with cross validation).
|
| Fig. 5: |
MSE values for prediction |
|
| Fig. 6: |
Active cost of criterion for T and CV for prediction |
|
| Fig. 7: |
Flexibility for classification and prediction |
This observation can be made on behalf of:
| • |
Complexity of problem |
| • |
Segmentation of larger datasets in smaller segments efficiently |
| • |
Distribution of variant cluster to segments |
| • |
Selection of best combination of architecture |
| • |
Availability of vast choice of sigmoid layers and number of preceptorns |
| • |
Any assignment between processing elements and clusters |
| • |
Cross validation with multi view analysis of criterion |
| • |
Assembly of chunks at time of need (if required according to nature of
problem being addressed) |
RESULTS AND DISCUSSION
Neoplasm classification and prediction with the help of dataset segmentation
in the form of larger and smaller combinations of clusters were demonstrated
with the help of discussions relating to cost factor, confusion matrix percentage,
percentage of training data for cross validation and training. The study opted
the way to categorize larger sets into segments of equal length with unequal
number of cluster of datasets. The different combinations helped better to visualize
neoplasm classification and prediction in terms of graphical presentation of
cost factors and confusion matrices. This small scale testing and validation
can be extended to any number of datasets with diverse number of segments and
clusters (scalabilit y factor of this enhanced mechanism). Table
4 demonstrates the cost factor values against variant combinations of clusters
for training and testing datasets. It is clear that with the increase of cross
validation and training datasets, a good percentage for malignant and benign
neoplasm categorization could be expected in terms of cost factor shown graphically.
This percentage fell down with lowering the clusters for T and CV. All cases
demonstrated feasible values of RMSE, because efficient calculations were performed
in the selection of optimal network architecture from a pool of larger and smaller
clusters of data. Similar work was carried by Chen et
al. (2002) who diagnosed the breast tumor using a combination of neural
network approach and wavelet transform. The segmentation algorithm takes advantage
from some features such as variance contrast, distribution distortion and auto
correlation contrast. The sonograms were analyzed using multilevel preceptors.
| Table 4: |
Diversity in cases for cost factor against combinations of
clusters |
 |
These network approaches were tested and evaluated using 242 cases with k-fold
cross validation for evaluation of performance. Also, Meinel
(2005) developed computer-aided diagnostic system for breast mri lesion
classification This investigation views also agree those of Wang
et al. (2010) who described a tumor classification method using probabilistic
neural network classifier with neighborhood rough set based gene reduction.
It is also important to concentrate the problems addressed in high concentration
and small size of samples in datasets. An iterative search model algorithm was
used for selection of initial set. Minimum gene subset was found by refinement
of this initial set. Ensemble classifier was constructed by using majority voting
strategy. The cross validation of results were made on single biomedical experiment.
Table 5 presents different combinations of larger and smaller
sized clusters with corresponding axon output values. Cluster 1 with data range
(training and testing) 10, 10 is more closely packed. The RMS value starts from
0.9 and fluctuates between 0.9 and 0.2. The major area of fluctuation is between
0.2 and 0.6. It represents an average error value for output axon. Case 2 shows
the same phenomenon between some 0.7 and 0.1.
| Table 5: |
Cluster range with output axon demonstration |
 |
|
Cases 3, 4 and 5 are almost similar with slight variations. Case 6 is another
depiction of case 1 and 2. Generally it was observed that the average number
of clusters (combination of both larger and smaller) for training and cross
validation provide more optimal solution for both classification and prediction
of neoplasm.
CONCLUSIONS
Computer aided tools and technologies play an important role in multi dimensional analysis of important biological data. This investigation addressed a challenging problem for classification and prediction of neoplasm in differentials of data segmentation and clustering. The results obtained in this process highlighted this as an enhanced and scalable approach with high level conceptual granularity. Instead of performing a whole analysis, the clinical data was segmented in variant clusters housed in equal sized chunks. Each segment was analyzed in parametric presentation of degree of prediction with appropriate selection of best neural combination concentrating features of cross validation, root mean square errors, active and confusion matrices. The enhanced approach obtained 100% results for benign and 99.5% for malignant with least feasible RMS value 0.017. The proposed enhanced approach was elaborated in detail with textual, experimental and graphical details with an added scalable feature of its applicability for any problem size and complexity.
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